Why does running Claude on Amazon Trainium matter for enterprise AI deployments?

The real benefit of Claude on Amazon Trainium for enterprise is not raw chip performance but improved supply, capacity, and price stability for inference workloads. Enterprises building production automations have historically struggled with throttling and unpredictable costs tied to GPU scarcity, and Trainium offers a credible path to more reliable, affordable access.

Why does my Power Automate flow keep getting slow or failing when using Claude?

The most common culprit is shared inference pool capacity, especially during peak hours, which causes latency spikes and occasional rate limit errors. Even with a retry policy in place, the delay is often long enough that users abandon the process before it completes.

How do I control Claude API costs in a high-volume Power Automate flow?

Treat your system prompt like code and review it regularly, because prompts tend to grow over time as edge cases and guardrails are added, multiplying your input token costs across every invocation. Moving context that is only relevant to certain topics into retrieval rather than including it in every call can significantly reduce per-run costs.

When should I start worrying about token cost drift in an AI automation?

Token cost drift can begin within weeks of deploying an agent as system prompts expand to handle new requirements and edge cases. The per-call cost often still looks small, so the problem tends to go unnoticed until the monthly total becomes difficult to justify.

What is Claude enterprise automation orchestration and how is it different from using Claude as a chatbot?

Claude enterprise automation orchestration means using Claude as the reasoning layer that manages multi-step workflows, rather than as a simple question-and-answer interface. Instead of responding to single prompts, Claude receives a complex goal, breaks it into steps, calls tools, evaluates results, and decides how to proceed. This requires stateful, branching logic that goes well beyond what a chat interface is designed to handle.

Why does instruction drift matter when using an LLM for workflow orchestration?

In orchestration, your system prompt acts as the process logic for the entire workflow, so if the model quietly forgets constraints or rules mid-execution, the whole process can break or produce incorrect outcomes. Some models begin losing adherence to instructions as context grows, which is a serious problem in long-running enterprise workflows. Consistency across extended sessions is one of the key reasons practitioners favour certain models for this use case.

When should I use an LLM as an orchestration layer instead of a traditional workflow tool?

An LLM-based orchestration layer becomes valuable when your workflow involves conditional reasoning, ambiguous inputs, or decisions that depend on synthesising information from multiple sources rather than following a fixed rule set. If your process logic can be fully mapped in advance and never changes based on context, a traditional workflow tool is likely simpler and more reliable. The LLM adds value where judgment and adaptability are required at execution time.

How does a large context window improve multi-step enterprise workflows?

A large context window allows the model to hold all relevant documents, intermediate results, and prior decisions in scope at once, rather than losing earlier information as the workflow progresses. This matters in processes that require a final decision to reference multiple earlier inputs, such as reviewing a contract alongside approval records and a policy document. Losing that context mid-run can lead to decisions that are inconsistent with earlier steps in the same workflow.

Tag: Claude

Anthropic Shipped Reflect With Claude and the Journaling Angle Is More Interesting Than It Looks
By Halilcan Soran · Power Platform & AI Agents · Senior Business Analyst at Visa · Published July 9, 2026

Anthropic just shipped Reflect with Claude, a guided journaling and self-reflection experience built on top of the Claude models. On the surface this reads as a wellness feature. Look at it for two more minutes and Reflect with Claude is a positioning move that tells you where Anthropic thinks the interaction pattern is going.

I want to talk about what it does, why I find it more interesting than the category suggests, and what I plan to actually do with it this week.

What it actually does

Reflect is a structured journaling surface inside Claude. You show up, Claude prompts you with reflection questions, and the session builds on prior sessions rather than starting from zero every time. It is opinionated about pacing. It asks follow-ups. It holds context across days.

The mechanics that matter, if you read past the wellness framing:
- The model prompts the user, not the other way around. That flips the default chat pattern.
- Memory persists across sessions in a scoped, purposeful way.
- The interaction is structured around a specific outcome, reflection, rather than open-ended tasks.
- Privacy defaults are tighter than a general Claude conversation, which Anthropic calls out directly.
You can try it on Claude.ai now. It is not an API surface, it is a product experience.

Why it matters for people who build agents

The wellness label is the wrapper. The interesting part is that Anthropic is publicly shipping a template for what I would call a companion pattern: model-initiated, memory-aware, purpose-bounded. That is not the same shape as a task assistant.

Most enterprise agents I see people build are task assistants. User asks, agent does, session ends. That works for a lot of internal automation. It falls apart the moment you want an agent that follows up, checks in, or maintains a thread across days without being re-prompted from scratch every time.

I have written before about how business logic and memory need real ownership in agent design. Reflect with Claude is a public reference point for what memory-aware, model-initiated interaction feels like when the vendor actually commits to it. The UX decisions in a product like this leak into what users start expecting from every agent they touch, including the ones I ship internally.

The positioning piece is worth naming too. Anthropic is signaling that Claude is not just a coding model or a knowledge worker copilot. It is a companion product. That is a different market than OpenAI is currently chasing with agents-as-workers, and it is a different market than Microsoft is chasing with Copilot Studio. If you follow the model landscape at all, this is the kind of move that tells you where the roadmap is pointed. I keep tracking these positioning shifts because they change what stakeholders ask for six months later. Anthropic’s approach to red-teaming and safeguards methodology is part of what makes a product like this credible at the enterprise level, and worth studying regardless of whether you are building consumer or internal agents.

My honest first take: this is more interesting than the launch post makes it sound. The category is wellness, the pattern is architecture.

What I would do with it this week

Three things, in order.

First, I am going to actually use it for a week. Not skim it, use it. I want to feel how the prompting cadence works, how the memory shows up in session two versus session five, and where the friction lives. You cannot design a companion pattern for internal agents if you have never used a shipped one end to end.

Second, I want to compare the interaction rhythm to what I have tried to build inside Copilot Studio agents. Copilot Studio is very good at request-response. It is not built for a model that opens the conversation. If I want an agent that pings a user with a reflection prompt at the end of the week, what does that actually look like on the Microsoft stack today? Probably a scheduled cloud flow that seeds a Copilot Studio conversation, which is a workaround, not the shape the platform was designed for. Worth stress testing. The reasons agents fail in production often come down to exactly this kind of mismatch between what you prototype and what the platform was actually designed to sustain.

Third, I want to write down which parts of the Reflect UX I would steal for internal use cases. Not the wellness angle. The structural parts. Session continuity, model-initiated prompts, bounded memory, tight privacy defaults. These are the pieces that generalize to things like weekly project retrospectives, onboarding check-ins, or post-incident reviews with an agent.

I cannot wait to try this properly. The launch might read as soft, but the shape underneath is the part I want to sit with.

Source: A new way to reflect on how you use Claude \ Anthropic (Anthropic).

About the author: Halilcan Soran is a Senior Business Analyst at Visa working on process optimization with Power Platform, RPA, and agentic AI. He previously led a 15-person team at Thomson Reuters, where the Power Automate solutions he built started his automation career. More about Halilcan · LinkedIn
July 9, 2026
Anthropic Dropped Claude Fable 5 Mythos 5 and the Long-Form Narrative Angle Is What Caught Me

By Halilcan Soran · Power Platform & AI Agents · Senior Business Analyst at Visa · Published June 9, 2026

Anthropic just shipped the Claude Fable 5 Mythos 5 release, a variant tuned specifically for long-form narrative and worldbuilding. You can read the announcement on the Anthropic news page. This is not a flagship capability bump. It is another specialized lineup entry, and the pattern is what caught my attention.

Finance Agents. Creative. Now Fable and Mythos. Anthropic is not just shipping bigger models. They are shipping shaped ones.

What it actually does

Fable 5 Mythos 5 is a Claude variant tuned for long-form fiction, worldbuilding, and narrative consistency. The headline improvements are in three areas.

First, long-context coherence. Holding a character voice, world rules, and plot threads across tens of thousands of tokens without drift is the actual hard problem in long-form generation. The model is tuned to keep internal consistency, names spelled the same way, magic systems that do not contradict themselves three chapters later, characters that do not forget what they did in chapter two.

Second, persona stability. Once you give it a narrator voice or a character voice, it holds it. Most general-purpose models start regressing toward a neutral helpful tone after a few thousand tokens. This one is trained to resist that pull.

Third, structural awareness. It understands scenes, beats, chapter structure, and pacing in a way that is closer to how writers actually think about their work, not as a flat stream of tokens.

This is not a model for code. It is not a model for tool use. It is a model for people who write novels, run tabletop campaigns, or build interactive narrative experiences.

Why it matters even if you are not writing fiction

I am not writing a fantasy novel this week. Most people reading this are not either. So why does this release matter?

Because the underlying improvements are exactly what agentic systems need.

Persona stability is the same problem as agent persona stability. If you are building a Copilot Studio agent that needs to talk like an HR specialist or a finance approver, the failure mode is identical: after enough turns, the agent regresses to generic assistant tone. The work Anthropic did to keep a narrator’s voice locked across 50,000 tokens is the same work that keeps your internal procurement agent from drifting into ChatGPT mode by message twelve.

Long-context coherence is the same problem as multi-turn agent memory. An agent that loses track of what it told the user three turns ago, or contradicts a constraint it acknowledged at the start of the session, is failing the same test a novel writing model fails when it changes a character’s eye color in chapter seven.

The specialization play is the real signal here. Anthropic is not selling one Claude. They are segmenting by job. As I wrote when Anthropic launched Claude Finance Agents, this is a deliberate move away from raw capability tiers and toward fit-for-purpose variants. That changes how you pick a model. Not “which is smartest” but “which is shaped for my job.”

This is also a hint about what the next round of enterprise variants will look like. If Fable and Mythos work commercially, expect more shaped Claudes. Legal. Medical. Compliance. The lineup is going to get longer, not shorter. The Anthropic enterprise AI services arm launching to deploy Claude in production is the infrastructure that makes this segmentation commercially viable at scale.

What I would do with it this week

Three things, in order.

One, read Anthropic’s own writeup carefully, not for the fiction angle but for what they say about persona retention and long-context evaluation. The eval methodology is the interesting part. If you build agents, those metrics are closer to what you should be measuring than HumanEval scores.

Two, run a quick comparison test on Claude. Take an agent system prompt you already have, one that defines a specific persona, and run a long multi-turn conversation on a general Claude variant. Note where the persona breaks. That gives you a baseline. When Fable Mythos style tuning eventually filters into general or enterprise variants, you will know what to measure.

Three, stop storing persona definitions in 4000-token system prompts. I have written about this before, and it stays true. If Anthropic is doing the persona work at the model level, your job is to give the model a tight, focused instruction set and let the tuning do the rest. A 400-token persona definition with clear constraints beats a 4000-token character bible every time. The same principle applies when you are thinking about whether to build a multi-agent system instead of a single agent: tighter scope and cleaner constraints outperform bloated configurations every time.

Anthropic is shipping shaped models faster than most teams can decide which one to use. That is a good problem to have.

Source: Claude Fable 5 and Claude Mythos 5 \ Anthropic (Anthropic).

About the author: Halilcan Soran is a Senior Business Analyst at Visa working on process optimization with Power Platform, RPA, and agentic AI. He previously led a 15-person team at Thomson Reuters, where the Power Automate solutions he built started his automation career. More about Halilcan · LinkedIn

June 9, 2026
Anthropic Acquired Stainless and the SDK Layer Just Became a Strategic Battleground

By Halilcan Soran · Power Platform & AI Agents · Senior Business Analyst at Visa · Published May 18, 2026

Anthropic just announced it has acquired Stainless, the company that auto-generates SDKs for OpenAI, Anthropic, and a long list of other API providers. The news that Anthropic acquires Stainless dropped this week and most of the commentary I have seen so far is treating it like a routine talent grab. I think that misses what is actually happening here.

This is Anthropic buying the layer that decides whether you actually ship on their platform or not.

What it actually does

Stainless is the tool a lot of AI labs use to turn an OpenAPI spec into idiomatic SDKs across Python, TypeScript, Go, Java, Kotlin, and Ruby. You write the spec once, Stainless generates clients that feel native in each language, handles auth, retries, streaming, pagination, and types. It is the thing that turns a raw HTTP API into something a developer can pip install and use in fifteen minutes.

Both the official Anthropic SDKs and the OpenAI SDKs have been generated by Stainless. So have SDKs for Cloudflare, Together, and others. The team built a real product around the boring but critical work of keeping client libraries in sync with API changes, generating typed responses, and producing docs that do not lie.

Anthropic is keeping Stainless running as a product and the existing customer relationships intact, at least for now. The team joins Anthropic. The IP comes with them.

Why it matters

The part I find most interesting is the strategic read. For years, the developer surface around an API was treated as overhead. You shipped a Python SDK, maybe a JS one, and called it done. Whoever wrote the cleanest curl examples won the docs game and that was the bar.

That bar is gone. When you are building agents, tool calling becomes the API. The shape of the SDK, how easy it is to define a tool, stream a response, handle a partial JSON object mid-stream, retry on a 429, all of that is what determines whether a developer ships or rage-quits. The SDK is no longer a wrapper. It is the product surface.

Anthropic seeing this clearly enough to acquire the team that builds it is, to me, a smarter move than most people will give it credit for. The model gets all the headlines but the SDK is what decides if your agent loop is 40 lines or 400. I have written before about how prototyping on the direct Anthropic API and lifting to Bedrock later burns a sprint. A big chunk of that pain is SDK-shaped. Region naming, model version strings, quota structures, retry behavior. Owning the SDK generator means Anthropic can make that migration story coherent instead of leaving it to whoever happens to be on call.

There is a real tension though. Stainless generates SDKs for OpenAI too. And for several other competitors. Anthropic says the existing customer relationships continue. I believe that for now. I do not believe it forever. At some point a feature lands in the Anthropic SDK two weeks before it lands in the OpenAI one, and people will notice. Neutrality in this part of the stack is going to be tested.

The other thing worth saying out loud: this is part of a pattern. Anthropic has been moving down the stack steadily. Trainium for compute. Finance agents and creative agents for vertical surface. Now SDK generation for the developer entry point. They are not just shipping a better model. They are building the full vertical from silicon to import statement.

What I would do with it this week

Concrete and short.

First, I would pin my current Anthropic SDK versions in any production code and write down the version numbers somewhere I will actually look. SDK velocity is about to go up. That is good news but it means breaking changes will move faster too. Pin and review on a schedule rather than letting Dependabot push silently.

Second, if you are still calling the Claude API with raw HTTP requests inside a Power Automate HTTP action or a custom connector, this is a good week to check whether the official SDK now covers your case. Custom connectors built around the SDK shape tend to age better than ones built around whatever curl example was on the docs page that day.

Third, if you have been on the fence about building tool calling into an agent, the ergonomics around tool definition in the Claude SDK are about to be the most invested-in part of the platform. Now is the time to start. I have been writing about this space long enough to know when a piece of infrastructure is about to get serious attention. This is one of those moments.

The SDK layer is where the next year of the AI developer experience will be won or lost. I am glad someone is finally treating it that way.

Source: Anthropic acquires Stainless \ Anthropic (Anthropic).

About the author: Halilcan Soran is a Senior Business Analyst at Visa working on process optimization with Power Platform, RPA, and agentic AI. He previously led a 15-person team at Thomson Reuters, where the Power Automate solutions he built started his automation career. More about Halilcan · LinkedIn

May 18, 2026
Anthropic Just Launched Claude for Small Business and the SMB Play Is Getting Serious

By Halilcan Soran · Power Platform & AI Agents · Senior Business Analyst at Visa · Published May 14, 2026

Anthropic shipped Claude for Small Business this week. A dedicated tier with admin controls, team billing, and collaboration features aimed squarely at companies that are too big for the personal plan and too small for the Enterprise sales motion. If you have been watching how frontier AI vendors reach the long tail of orgs, claude for small business is a signal worth paying attention to.

For years, SMBs lived on Microsoft 365 plus a pile of spreadsheets and maybe a Power Automate flow somebody built once and never touched again. Anthropic just walked into that space with a real product, not a discount on the Pro plan.

What it actually does

The new tier gives small businesses a Claude workspace with team management, centralized billing, and admin controls. You get the same Claude models the rest of the market uses, but wrapped in the boring infrastructure that actually matters when more than one person needs access: user provisioning, usage visibility, and a single invoice instead of five personal subscriptions billed to different cards.

It is positioned between the individual Pro plan and the full Enterprise contract. No procurement cycle, no security questionnaire, no minimum seat commitment that scares off a ten-person company. You sign up, add your team, and you are running.

The feature list is not exotic. That is the point. Anthropic is not trying to wow SMBs with new model capabilities. They are removing the friction that kept Claude out of small business workflows entirely.

Why it matters

The interesting part is not the product. It is the strategic move.

Frontier AI vendors have spent the last two years chasing Fortune 500 logos. Custom contracts, dedicated capacity, named account teams. Anthropic’s enterprise services arm made that play explicit. But the F500 is a few thousand companies. The SMB segment is millions. And those companies make tooling decisions in days, not quarters.

Microsoft has owned this segment by default. Copilot for Microsoft 365 lives where SMBs already work. Email, documents, Teams. Anthropic showing up with a focused product means the workspace conversation is no longer a Microsoft monologue. A ten-person agency can now pick Claude as their primary AI tool without feeling like they are going off-script.

The bigger second-order effect is what mid-market buyers will start asking for. SMBs adopt fast and complain loud. The patterns that emerge here, the integrations they wire up, the workflows they build, will shape what a 500-person company asks for eighteen months from now. I have written before that vendor stratification was coming for AI tooling the way it came for SaaS a decade ago. This is what it looks like in practice. Anthropic has already shown it will compete on vertical specialization too — the Claude Finance Agents launch is a good example of how the segmentation strategy is playing out across different buyer types.

There is a risk worth naming. SMBs that adopt Claude as their primary AI surface and then grow into mid-market will be running a workspace that does not natively live inside their Microsoft tenant. That integration gap is real. It is also exactly the kind of gap that makes a third-party connector market explode.

What I would do with it this week

If I were running a small operation right now, I would do three things.

First, I would actually try the tier with a real team, not a solo account. The admin and billing experience is where these products live or die for small businesses, and you cannot evaluate it alone.

Second, I would map which workflows currently live in spreadsheets and shared inboxes. Those are the workflows that move to Claude first in an SMB. Not the polished automation candidates, the messy ones nobody owns. The places where a capable assistant with team context replaces a process that was never documented.

Third, I would think hard about the integration surface. Claude on its own is a chat product. The value compounds when it touches your actual data and tools. For a small business that already runs on Microsoft 365, that means either calling Claude from a Power Automate flow through the API, or accepting that the Claude workspace and the Microsoft workspace will live in parallel for a while. Both are fine. Pretending you do not need to pick is not. If you are evaluating how the two model ecosystems compare for automation use cases specifically, Claude vs ChatGPT for automation is worth reading before you commit to a direction.

The addressable market for capable agents just got wider. Watch what the long tail builds with it.

Source: Introducing Claude for Small Business \ Anthropic (Anthropic).

About the author: Halilcan Soran is a Senior Business Analyst at Visa working on process optimization with Power Platform, RPA, and agentic AI. He previously led a 15-person team at Thomson Reuters, where the Power Automate solutions he built started his automation career. More about Halilcan · LinkedIn

May 14, 2026
Anthropic Raised Claude Rate Limits for SpaceX and That Tells You Where Enterprise AI Is Heading

By Halilcan Soran · Power Platform & AI Agents · Senior Business Analyst at Visa · Published May 6, 2026

Anthropic announced a deal with SpaceX that includes higher Claude rate limits as part of the engagement. The headline most people will read is the SpaceX logo. The actual signal is different. The anthropic spacex higher rate limits story tells you that capacity is now a negotiated enterprise lever, not a number on a pricing page.

If you are building agents seriously, this is the part to pay attention to.

What it actually does

The deal gives SpaceX elevated rate limits on Claude, alongside the usual enterprise engagement wrapping. Anthropic frames it as supporting frontier engineering work where teams need sustained throughput for code generation, document analysis, and agent loops at scale.

The published tiers on the Anthropic site still exist. Usage tier 1, tier 2, tier 3, tier 4 with their requests-per-minute and input-tokens-per-minute caps. What this announcement quietly confirms is that above those tiers, the conversation is bespoke. You sign a contract, you get a number that fits your workload.

That has been true behind the scenes for a while. Saying it out loud, with a customer name attached, is the new part.

Why it matters

Most teams I talk to still treat rate limits like an afterthought. They build a prototype on a developer key, the latency feels fine, the cost looks reasonable, and they move toward production. Then a real workload hits and the 429s start.

I wrote about this in a different shape when I covered Claude running on Amazon Trainium. The benchmark conversation distracts from the real production failure mode, which is capacity drift at peak hours. Rate limits are the same story from a different angle. Throughput is now part of the architecture, not a footnote.

Three things follow from this.

First, the gap between what a hobbyist API key can do and what a serious enterprise workload needs is widening fast. A single agent loop with tool calls, retries, and a few sub-agents can burn through a tier 2 limit in seconds. Multi-agent orchestration makes it worse. If you are running ten parallel agent invocations from a Power Automate flow, each with their own context window, you will hit the ceiling before you hit the budget. If you are thinking through whether your workload even needs that kind of parallelism, the honest answer on single-agent vs multi-agent design is worth reading before you scale out.

Second, procurement now needs to ask different questions. Not just price per million tokens. Sustained tokens per minute. Concurrency. Burst tolerance. Region. What happens when your traffic doubles next quarter. Most enterprise AI contracts I hear about from people at other organisations still get signed without these numbers nailed down. Anthropic is moving further in this direction across the board, and their enterprise AI services arm is exactly the context in which these bespoke capacity conversations are going to happen.

Third, the platform vendors are going to feel pressure here. If you are running Claude through Bedrock or through Copilot Studio, your effective rate limit is shaped by both Anthropic and the platform layer. The platform abstracts capacity, which is convenient until it is not. Knowing where the ceiling actually sits in your stack is going to matter more, not less.

What I would do with it this week

If you are running anything beyond a demo, instrument the throughput. Not just success and failure counts. Tokens per minute, requests per minute, p95 latency, and 429 rate, broken down by flow or agent. You cannot negotiate a number you have not measured.

Then look at your system prompts. Token bloat is the cheapest capacity win available. A prompt that drifted from 800 to 4000 tokens over a few sprints is not just costing you money, it is eating your throughput ceiling on every single call. I have seen this kill production agents at peak hours when nobody changed the model or the workload.

Then map your workload to a tier. If your steady state is comfortably inside a published tier, fine. If you are within forty percent of the ceiling on any axis, you are already in negotiation territory. Start the conversation before the incident, not after.

For teams building on Power Platform with Claude in the loop, the same logic applies through whatever connector or custom action you are using. Concurrency settings on a Power Automate flow can mask the real call pattern until it does not. Know what your worst minute looks like.

The SpaceX deal is a marker. Capacity has joined price and capability as a first-class procurement axis for enterprise AI, and the teams treating it that way now will have one less surprise next year. (My ongoing notes on this stuff live on my LinkedIn if you want to follow along.)

Source: Higher usage limits for Claude and a compute deal with SpaceX \ Anthropic (Anthropic).

About the author: Halilcan Soran is a Senior Business Analyst at Visa working on process optimization with Power Platform, RPA, and agentic AI. He previously led a 15-person team at Thomson Reuters, where the Power Automate solutions he built started his automation career. More about Halilcan · LinkedIn

May 6, 2026
Anthropic Just Launched Claude Finance Agents and the Specialization Trend Is Real

By Halilcan Soran · Power Platform & AI Agents · Senior Business Analyst at Visa · Published May 5, 2026

Anthropic shipped Claude Finance Agents this week. A set of Claude-powered agents built specifically for financial analysts, with native connectors to LSEG, Moody’s, S&P Global, and Morningstar. This is not a generic chat assistant pointed at a finance prompt. The Claude finance agents are a packaged product with the data plumbing already done.

I have been watching this trend build for months. This release makes it impossible to ignore.

What Anthropic actually shipped

Three agents wrapped around specific analyst workflows. One handles modeling. One handles due diligence. One handles comparable company analysis. Each one is a Claude agent with a defined scope, a system prompt tuned to that workflow, and direct connectors into the data providers analysts already pay for.

The connector list is the part that matters. LSEG for market data. Moody’s for credit. S&P Global for company financials. Morningstar for funds. These are not scraped sources. They are licensed enterprise feeds. Anthropic did the integration work that every internal team building a finance copilot would otherwise have to do themselves.

You can read the full announcement on Anthropic’s site. The pricing structure is enterprise. The target user is clear. This is not a consumer move.

Why this release matters beyond finance

For two years the pitch from foundation model vendors was: here is the model, build whatever you want. That is over.

Now the pitch is: here is the model, here is the agent, here are the connectors, here is the workflow. The vendor is moving up the stack into the application layer. Anthropic is doing it for finance. Microsoft is doing it for general enterprise productivity. OpenAI is doing it for coding and research. The pattern is consistent. Anthropic launching an enterprise AI services arm was an early signal of exactly this direction.

This changes the build-vs-buy math in a real way. If you are an enterprise team that was about to spend six months building a Claude-based comparable company analysis agent on top of a generic platform, you now have to ask whether your custom version will actually beat what Anthropic ships out of the box. Most of the time, in the specific domains where these vertical agents land, the answer will be no.

That does not mean custom builds are dead. It means the line moves. Custom builds make sense where the vendor product does not exist or does not match your specific data and policies. Generic finance modeling? Probably not worth building. Your firm’s specific deal screening logic with your proprietary scoring model? Still custom.

The other thing this release confirms is that tool design is product design. I have written before that agentic workflows live or die on the quality of their tool layer. Anthropic clearly figured this out. Wrapping LSEG and S&P data with proper structured outputs that Claude can reason about is the actual hard work. Anyone who has tried to build this on top of raw connectors knows.

This specialization pattern also raises a real architectural question: when the vendor ships a domain-specific agent, does your orchestration layer treat it as a peer, a sub-agent, or a replacement? That is the same question I work through in when to build a multi-agent system instead of a single agent.

What I would do with it this week

I do not work in finance, so I am not deploying this in production. But here is what I would do if I were on a finance team, and what I am doing in adjacent domains.

First, audit any internal agent project that overlaps with what Anthropic just shipped. If a team has been building a comparable company analysis tool for four months and Anthropic just released one, that conversation has to happen now, not in Q3.

Second, look at the connector list and ask which of those data sources your team already licenses. The value of Claude Finance Agents drops fast if you do not have LSEG or S&P feeds. Vendor lock through data integration is the real moat here.

Third, think about what the equivalent looks like in your domain. If Anthropic shipped finance agents in May 2026, what does an HR agent product look like? A legal one? A procurement one? Someone is building each of these. Probably more than one someone. In my experience, the teams that win the build-vs-buy decision are the ones that ask the question early, not the ones that finish their custom build and then discover the vendor product. The same specialization logic is visible in Microsoft Discovery as the first real glimpse of domain-specific agent platforms.

For Power Platform builders, this is also a useful signal. Copilot Studio is Microsoft’s answer to the same trend, and the business skills work in Dataverse is the integration layer equivalent. The shape of the market is clear.

The era of generic agent platforms competing on model quality alone is closing. The next round is about who owns the workflow.

Source: Agents for financial services \ Anthropic (Anthropic).

About the author: Halilcan Soran is a Senior Business Analyst at Visa working on process optimization with Power Platform, RPA, and agentic AI. He previously led a 15-person team at Thomson Reuters, where the Power Automate solutions he built started his automation career. More about Halilcan · LinkedIn

May 5, 2026
Anthropic Is Launching an Enterprise AI Services Arm and That Changes the Vendor Conversation

By Halilcan Soran · Power Platform & AI Agents · Senior Business Analyst at Visa · Published May 4, 2026

Anthropic announced it is standing up a dedicated enterprise AI services company to help large organizations deploy Claude in production. This is the kind of move that does not look loud on a Monday but reshapes how anthropic enterprise ai services conversations go inside large orgs for the next two years.

Until now, if you wanted hands-on Anthropic help inside your organisation, you went through a partner, hired a boutique, or figured it out yourself. That is changing.

What Anthropic actually announced

Anthropic is launching a services arm focused on enterprise deployment. Not just API access. Not just Claude for individuals. A real services org built to sit next to enterprise teams and help them stand up Claude inside production environments.

That means architecture work, integration help, deployment patterns, and the kind of hands-on engagement that used to belong exclusively to Microsoft Consulting Services, Accenture, Deloitte, and the big SI bench. Anthropic is now in that conversation directly.

It is worth being precise here. This is not Anthropic becoming a generic consultancy. The framing is narrower: help enterprises actually deploy Claude in production for high-value use cases. That is a much sharper offer than “AI transformation,” and it is the kind of focus that tends to ship working systems instead of slide decks.

Why this matters for enterprise AI buyers

For most of 2024 and 2025, if you were building AI automation inside a large enterprise, your reference architecture defaulted to the Microsoft stack. Azure OpenAI, Copilot Studio, Power Platform, the whole vertical. That is not because Microsoft is always the best fit. It is because Microsoft has the procurement story, the EA discount, the field engineers, and the reference architectures already on the shelf.

Anthropic just moved to close that gap.

A credible second source for enterprise AI deployment work changes three things in real procurement conversations. First, you can now run a genuine bake-off where the non-Microsoft option has hands-on support, not just a model endpoint. Second, reference architectures stop being Microsoft-shaped by default. Third, the negotiation leverage shifts. When the only credible vendor is also your cloud provider, your CRM, your collaboration suite, and your AI copilot, you are not really negotiating.

I have written before about why the Bedrock vs direct Anthropic API question is a governance decision, not a model decision. This announcement is the next step on that same line. Anthropic is acknowledging that getting Claude into a regulated enterprise is not a model problem. It is a deployment problem. They are now staffing for that reality.

The honest part: services orgs at model companies are hard. The talent market for AI engineers who can also navigate enterprise IT is brutal. Anthropic will get pulled into pre-sales work, RFP responses, and stakeholder meetings that consume engineering capacity. Whether they keep the focus tight or drift into generic consultancy is the open question.

What I would do with this news this week

If you are anywhere near AI procurement or architecture decisions, three concrete things.

First, put Anthropic on your shortlist for the next AI deployment review. Not as a model. As a deployment partner. The conversation is now legitimately different from “call the AWS rep about Bedrock.”

Second, revisit your reference architectures. If yours quietly assumes the Microsoft stack from end to end, write down why. Some of those reasons will hold up. Some will turn out to be “because that is what the last project did.” Those are the ones to challenge.

Third, if you are a Power Platform shop, this does not mean ripping anything out. Copilot Studio, Power Automate, and the Microsoft surface area are still where citizen development happens. But the heavy orchestration brain behind your agents does not have to be Azure OpenAI by default. I have been thinking about this for months, and as I covered in Claude vs ChatGPT Is the Wrong Question When You Are Building Automations, the model choice sitting behind your flows is less important than the deployment and governance story around it. It is now a real option with real support behind it.

The interesting enterprise AI conversations for the rest of this year are not going to be about which model wins a benchmark. They are going to be about who shows up when you need to ship. From what I see in the community, that is exactly the conversation Anthropic just inserted itself into.

And if the services arm delivers on its framing, it will also change how multi-agent orchestration patterns get designed in the first place — because you will finally have an Anthropic-native team in the room when those architecture decisions get made.

The vendor field just got more interesting.

Source: Building a new enterprise AI services company with Blackstone, Hellman & Friedman, and Goldman Sachs \ Anthropic (Anthropic).

About the author: Halilcan Soran is a Senior Business Analyst at Visa working on process optimization with Power Platform, RPA, and agentic AI. He previously led a 15-person team at Thomson Reuters, where the Power Automate solutions he built started his automation career. More about Halilcan · LinkedIn

May 4, 2026
Anthropic Just Launched Claude for Creative Work and It Is a Real Positioning Move

By Halilcan Soran · Power Platform & AI Agents · Senior Business Analyst at Visa · Published April 28, 2026

Anthropic shipped Claude for Creative Work this week. It is a dedicated offering aimed at writers, designers, and other creative professionals. I spend most of my day in the automation side of AI, but I want to talk about this one because the positioning is more interesting than the product itself.

This is the first time I have seen a major lab carve out creative work as a first-class lane, separate from coding and enterprise automation. That is a signal worth paying attention to.

What Claude for Creative Work actually does

The release packages Claude around the workflows that creative professionals actually run. Long-form drafting, editorial revision, voice and style consistency across a body of work, brainstorming with a model that pushes back instead of agreeing with everything. There are tighter integrations for the tools writers and designers already live in, and the framing leans heavily on Claude’s writing quality rather than benchmark scores.

It is not a new model. It is a new product wrapper around Claude with prompts, defaults, and surfaces tuned for creative output. Think of it as Anthropic saying: we know writers were already using us, here is the version built for them.

The technical novelty is limited. The positioning novelty is not.

Why this segmentation matters

For the last two years, model vendors have sold one product to everyone. Same Claude for the lawyer, the developer, the marketing copywriter, the Power Platform builder. Differentiation happened at the system prompt layer, which meant every team had to figure out their own configuration from scratch.

Anthropic is now segmenting by job-to-be-done. Claude for coding. Claude for enterprise. Claude for creative work. This is closer to how Adobe, Microsoft, and Salesforce package software, and it is a meaningful shift in how AI gets sold and bought.

Two things follow from this.

First, procurement gets easier for non-technical buyers. A marketing director does not want to evaluate a foundation model. They want to know if the tool fits their team’s workflow. A clearly named product solves that.

Second, the system prompt and tooling work that used to be invisible becomes the actual product. I wrote about this when I covered Claude on Trainium: prompt engineering is production code, and the cost of treating it casually compounds. Anthropic is now productising that layer for one specific audience. I expect the same move for legal, research, and analytics in the next year.

The interesting question for anyone in automation is whether the enterprise lane gets the same treatment. A Claude for Enterprise Automation with first-class tool calling defaults, audit logging, and connection-scoped caching would do more for the agentic workflow problem than another model bump. If you are thinking about how Claude already fits into that orchestration layer, Claude as an Orchestration Brain Is the Most Interesting Thing Happening in Enterprise AI Right Now is worth reading alongside this one.

What I would do with it this week

I am not a creative professional. But drafting is a real part of my week, and I want to test this against my current setup.

I would use Claude for Creative Work to draft long-form posts and technical explainers, then compare the output against the same prompts run through the standard Claude interface. The thing I want to know is whether the tuned defaults actually change the output meaningfully, or whether a good system prompt gets you 90 percent of the way there. My bet is on the latter, but I want to be wrong.

I would also try it on editorial revision passes. Take a 1500-word draft I already wrote, run it through with instructions to tighten and remove filler, and see how it handles voice preservation. This is where Claude has historically been strong, and a product surface tuned for it should make the loop faster.

One thing I will not do is force this into a Power Platform flow. Power Automate already has decent options for content generation through the existing Claude and OpenAI connectors. A creative-tuned product surface does not change the connector story. If you are weighing which model to point at which workload inside those flows, Claude vs ChatGPT Is the Wrong Question When You Are Building Automations covers exactly that tradeoff.

The real takeaway is not the product. It is the precedent. Vendors are starting to segment foundation models by job-to-be-done, and that changes how I will think about which model lane to point at which workload going forward. More on that as the other lanes ship. I have been tracking this shift closely because it changes the buying conversation as much as the building one.

Watching to see if Anthropic ships a creative-equivalent for enterprise automation next.

Source: Claude for Creative Work \ Anthropic (Anthropic).

About the author: Halilcan Soran is a Senior Business Analyst at Visa working on process optimization with Power Platform, RPA, and agentic AI. He previously led a 15-person team at Thomson Reuters, where the Power Automate solutions he built started his automation career. More about Halilcan · LinkedIn

April 28, 2026
Anthropic Running Claude on Trainium Matters More for Enterprise Than the Benchmarks Suggest

By Halilcan Soran · Power Platform & AI Agents · Senior Business Analyst at Visa · Published April 25, 2026

Most of the coverage of Anthropic running Claude on Amazon’s Trainium chips frames it as a benchmark race. Faster training. Cheaper inference. Another shot at Nvidia. That framing misses what actually matters if you are building production automations. The thing that should make enterprise Power Platform and AI people pay attention to claude on amazon trainium for enterprise is not raw performance. It is supply, capacity, and price stability.

I have been building Claude-backed flows internally for a while now. The model quality has not been my problem. The economics and the throttling have.

Why the Trainium Story Is Actually a Capacity Story

When you read about Anthropic moving serious training and inference onto Trainium, the interesting part is not whether the chip beats an H100 on some synthetic benchmark. The interesting part is that for the first time there is a credible path to Claude pricing that is not entirely tied to Nvidia GPU scarcity.

If you have ever tried to scale a customer-facing agent on a shared inference pool, you know what I mean. Peak hours hit. Latency drifts up. Occasionally you get a 429. Your Power Automate flow has a retry policy, sure, but the user already saw the spinning circle for nine seconds and moved on.

Capacity is the silent killer. Benchmarks are the loud distraction.

What Token Cost Drift Looks Like in a Real Power Automate Flow

Here is the thing nobody tells you when you slot Claude into a flow through Bedrock or the API. The first version of your agent has a tight system prompt. Maybe 800 tokens. Then someone asks for a new edge case. You add a few examples. Then someone reports a wrong answer, so you add a guardrail paragraph. Then you add tool descriptions. Then you add a few more examples because the tool descriptions confused the model.

Six weeks later your system prompt is 4,200 tokens. Every single invocation pays for those tokens. If your flow runs 12,000 times a month, you just multiplied your input cost by five and nobody noticed because the per-call cost still looks tiny on the invoice.

I learned this the hard way on an internal agent. The unit cost looked fine. The monthly bill did not. The fix was not switching models. The fix was treating the system prompt like code, with a review step, and splitting context that only some topics need into retrieval rather than baking it into every call.

This is the part where chip economics actually touch your Power Automate flow. If inference cost per token drops because Anthropic has cheaper compute, that bloated prompt hurts less. If it does not drop, your business case erodes quietly while you build more features on top.

Bedrock vs Direct Anthropic API for Enterprise Automation Workloads

People ask me which one to use. The honest answer is it depends on what your governance team will sign off on, not what the model does.

Bedrock gives you the AWS contract, the data residency story, the IAM model your security team already understands, and provisioned throughput as an option. The direct Anthropic API gives you faster access to new models and sometimes better pricing on burst usage.

For anything customer-facing or anything that touches regulated data, Bedrock usually wins on the paperwork alone. For internal experimentation and prototypes, the direct API is fine. The mistake I see people make is prototyping on the direct API and then trying to lift and shift to Bedrock at the last minute. Region availability, model version naming, and quota structure are different enough that you will burn a sprint on it.

Pick the path your production version will live on. Build there from day one. If you are still weighing how Claude fits into your automation architecture more broadly, Claude as an Orchestration Brain Is the Most Interesting Thing Happening in Enterprise AI Right Now covers where it actually earns its place as a reasoning layer.

How I Would Plan Claude Capacity for an Agent You Actually Depend On

If the agent matters, on-demand inference is not enough. Provisioned or reserved capacity is starting to look less like a luxury and more like a baseline. Latency Is the Quiet Killer of Agentic Workflows and Almost Nobody Talks About It goes into how to budget round-trip time before you build, and the same logic applies to capacity. A flow that works at 2pm on Tuesday and times out at 10am on Monday is not a production system. It is a demo with good luck.

Three things I would actually do.

Measure your real token distribution. Not the average. The 95th percentile input and output. That is what your capacity needs to handle, not the median case.

Separate your workloads. The agent that drafts an email for an internal user can sit on shared on-demand inference. The agent that responds to a customer in under three seconds cannot. Different SLAs, different capacity tiers.

Track cost per successful outcome, not cost per call. An agent that fails 20 percent of the time and gets retried is twice as expensive as the invoice suggests. This is where bad tool design quietly destroys your unit economics. If you are unsure whether the model choice even matters as much as you think for automation workloads, Claude vs ChatGPT Is the Wrong Question When You Are Building Automations is worth reading before you optimize the wrong variable.

The Trainium news matters because it changes the long-term curve on what any of this costs. But the curve only helps you if your architecture is set up to benefit from it. Bloated prompts, on-demand only inference, and no measurement of cost per outcome will eat any savings the chip story delivers.

Read the news through that lens. The benchmarks are not the point.

Frequently Asked Questions

Why does running Claude on Amazon Trainium matter for enterprise AI deployments?

The real benefit of Claude on Amazon Trainium for enterprise is not raw chip performance but improved supply, capacity, and price stability for inference workloads. Enterprises building production automations have historically struggled with throttling and unpredictable costs tied to GPU scarcity, and Trainium offers a credible path to more reliable, affordable access.

Why does my Power Automate flow keep getting slow or failing when using Claude?

The most common culprit is shared inference pool capacity, especially during peak hours, which causes latency spikes and occasional rate limit errors. Even with a retry policy in place, the delay is often long enough that users abandon the process before it completes.

How do I control Claude API costs in a high-volume Power Automate flow?

Treat your system prompt like code and review it regularly, because prompts tend to grow over time as edge cases and guardrails are added, multiplying your input token costs across every invocation. Moving context that is only relevant to certain topics into retrieval rather than including it in every call can significantly reduce per-run costs.

When should I start worrying about token cost drift in an AI automation?

Token cost drift can begin within weeks of deploying an agent as system prompts expand to handle new requirements and edge cases. The per-call cost often still looks small, so the problem tends to go unnoticed until the monthly total becomes difficult to justify.

About the author: Halilcan Soran is a Senior Business Analyst at Visa working on process optimization with Power Platform, RPA, and agentic AI. He previously led a 15-person team at Thomson Reuters, where the Power Automate solutions he built started his automation career. More about Halilcan · LinkedIn

April 25, 2026
Claude as an Orchestration Brain Is the Most Interesting Thing Happening in Enterprise AI Right Now

By Halilcan Soran · Power Platform & AI Agents · Senior Business Analyst at Visa · Published April 17, 2026

Most of the conversation around Claude in enterprise automation circles is stuck on the wrong question. People are comparing it to GPT-4o or Gemini as a text generator, debating which one writes better emails or summarises documents more accurately. That framing completely misses what makes Claude enterprise automation orchestration genuinely interesting right now.

The practitioners I talk to who are getting real results are not using Claude as a chatbot. They are using it as the reasoning layer that decides what to do next in a multi-step, stateful workflow. That is a different problem than answering a question, and it changes everything about where Claude fits in your architecture.

The chatbot framing is getting in the way

When a team says they want to “add Claude” to something, the default mental model is a chat interface. User sends message, model replies. Maybe it calls a tool or two. That is not orchestration. That is a smarter input box.

Orchestration is what happens when you need a model to receive a complex goal, break it into sequenced steps, call different tools at different points, evaluate intermediate results, and decide whether to continue, retry, or escalate. The model is not answering a question. It is managing execution across a process that has state, has branching conditions, and has consequences if it goes wrong.

I wrote about this problem directly in the post on agentic workflows. The LLM is not the agent. The LLM is the reasoning layer. If you treat them as the same thing, you end up bolting a model onto the response step of what is really just a structured flow. That is not orchestration. That is decoration.

What makes Claude specifically interesting for orchestration logic

Two things stand out when I look at how Claude behaves in multi-step contexts compared to other models at similar capability levels.

First, instruction following under load. When you give Claude a detailed system prompt with conditional logic, constraints, tool-use rules, and output format requirements, it holds those instructions across a long session more reliably than most alternatives. With other models I have tested, instruction drift starts showing up once you push past a few thousand tokens of context. Claude handles longer, more complex prompts without silently dropping constraints mid-execution. For orchestration, where the system prompt is essentially your process logic written in natural language, that matters a lot.

Second, the extended context window is not just about volume. It is about statefulness. A workflow that processes a contract, then a set of approval records, then a policy document, then makes a decision that references all three needs a model that can hold all of that in scope simultaneously. Losing context partway through an orchestration run means the model makes decisions with incomplete information. It does not know it has incomplete information. It proceeds confidently anyway. I have seen exactly this failure mode in Copilot Studio agents, where silent context loss leads to confident-sounding responses for tasks that were never properly evaluated.

Where I would actually slot this into a Power Platform architecture

I would not replace the existing orchestration layer in a Power Automate flow with a Claude prompt. That is not the use case. Power Automate is still the right place for deterministic, sequential steps with connectors, triggers, and error handling you can inspect.

Where Claude earns its place is in the decision layer that sits above or between those steps. Think of a workflow that processes incoming requests, where each request has variable structure, ambiguous intent, and routing logic that depends on context that changes week to week. A hard-coded set of conditions in Power Automate will break the moment the business logic shifts. A Claude orchestration layer that reads the request, evaluates the current context loaded from Dataverse, and decides which downstream flow to invoke handles that variability without you rewriting conditions every time.

In practice, I would build it as a Copilot Studio agent backed by Claude through a custom connector or direct API call, where Claude handles the reasoning and routing logic and Power Automate handles execution of the discrete steps. The agent decides. The flows act. The separation matters because it keeps your execution logic testable and your reasoning logic flexible. Before wiring any of this together, it is also worth auditing what adding Copilot to an existing app actually changes versus what it just surfaces differently.

The governance piece from the post on enterprise Power Platform applies here too. Calling an external Anthropic API endpoint means your orchestration reasoning is leaving the tenant. That is an audit trail split and a DLP conversation you need to have before you build, not after.

The honest constraints before you redesign anything

Claude is not a free variable. Longer context windows mean higher token costs per run, and orchestration workflows that run hundreds of times a day will surface that quickly in billing. Model latency at high context volumes is also real. If your process requires sub-second decisions, this is not your tool.

The other constraint is testability. When your orchestration logic lives in a system prompt rather than a flow diagram, reproducing a failure is harder. The model made a bad routing decision on Tuesday afternoon. Why? You need logging at the prompt level, not just at the action level. Most teams I see building this way have not set that up, and they hit the same silent failure problem I described in the Copilot Studio testing post: everything looks fine until a real user finds the edge case.

Claude as an orchestration brain is a genuinely different capability than what most teams are building with today. The question is not whether it is smarter than the last model. The question is whether your architecture is designed to use a reasoning layer at all, or whether you are still just looking for a better chatbot to put at the front of a process that was never designed to be orchestrated.

Frequently Asked Questions

What is Claude enterprise automation orchestration and how is it different from using Claude as a chatbot?

Claude enterprise automation orchestration means using Claude as the reasoning layer that manages multi-step workflows, rather than as a simple question-and-answer interface. Instead of responding to single prompts, Claude receives a complex goal, breaks it into steps, calls tools, evaluates results, and decides how to proceed. This requires stateful, branching logic that goes well beyond what a chat interface is designed to handle.

Why does instruction drift matter when using an LLM for workflow orchestration?

In orchestration, your system prompt acts as the process logic for the entire workflow, so if the model quietly forgets constraints or rules mid-execution, the whole process can break or produce incorrect outcomes. Some models begin losing adherence to instructions as context grows, which is a serious problem in long-running enterprise workflows. Consistency across extended sessions is one of the key reasons practitioners favour certain models for this use case.

When should I use an LLM as an orchestration layer instead of a traditional workflow tool?

An LLM-based orchestration layer becomes valuable when your workflow involves conditional reasoning, ambiguous inputs, or decisions that depend on synthesising information from multiple sources rather than following a fixed rule set. If your process logic can be fully mapped in advance and never changes based on context, a traditional workflow tool is likely simpler and more reliable. The LLM adds value where judgment and adaptability are required at execution time.

How does a large context window improve multi-step enterprise workflows?

A large context window allows the model to hold all relevant documents, intermediate results, and prior decisions in scope at once, rather than losing earlier information as the workflow progresses. This matters in processes that require a final decision to reference multiple earlier inputs, such as reviewing a contract alongside approval records and a policy document. Losing that context mid-run can lead to decisions that are inconsistent with earlier steps in the same workflow.

About the author: Halilcan Soran is a Senior Business Analyst at Visa working on process optimization with Power Platform, RPA, and agentic AI. He previously led a 15-person team at Thomson Reuters, where the Power Automate solutions he built started his automation career. More about Halilcan · LinkedIn

April 17, 2026