Tag: Anthropic

Tino Cuellar Joining Anthropic’s Board Is a Governance Signal

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

On 3 August 2026, Anthropic announced that Mariano-Florentino (Tino) Cuellar, president of the Carnegie Endowment for International Peace and a former California Supreme Court justice, is joining the Anthropic board of directors. The appointment came through the company’s Long-Term Benefit Trust, the governance mechanism Anthropic built specifically to seat directors who are not financially tied to the company. For anyone deploying these models inside an enterprise, this matters because board composition sits upstream of every usage policy and deployment constraint that eventually lands in your agents.

This is not a product release. There is no benchmark table to read twice. But I think it deserves the same attention as one, because governance moves like this one shape what the products are allowed to do two years from now.

Tino Cuellar Joins Anthropic’s Board of Directors as a Jurist, Not an Investor

Look at who typically sits on the board of an AI lab. Founders, lead investors, maybe an operator from a previous portfolio win. The incentive structure is uniform: grow the company, protect the equity. That is normal and I am not pretending it is scandalous. It is just narrow.

Cuellar breaks the pattern. He spent years as a justice on the California Supreme Court, taught at Stanford, worked on domestic policy in the Obama White House, and now runs the Carnegie Endowment, one of the oldest international affairs think tanks in existence. His entire career is about how institutions constrain power, across borders, under law. Putting someone with that profile in a director seat, with actual voting rights rather than an advisory title, is a different move from hiring a policy VP. Advisors get consulted. Directors get to say no.

The Long-Term Benefit Trust Doing What It Was Designed For

The mechanism matters more than the name. Anthropic set up the Long-Term Benefit Trust years ago: a group of trustees with no financial stake in the company, holding the power to elect a growing share of the board. When it launched, plenty of people dismissed it as governance theater. A structure that exists on paper but never gets exercised is exactly that.

This appointment is the structure getting exercised. The LTBT chose a jurist and international policy scholar, not another tech operator, and the company seated him. That is the difference between a governance document and a governance practice. I made a similar argument when Anthropic published the Fable 5 rollback postmortem: the public artifact is the contract. A trust that visibly places independent directors is a stronger commitment than any responsible AI pledge on a marketing page, because it is structurally hard to walk back. Anthropic has shown a similar instinct in how it approaches safety methodology — the Fable jailbreak safeguards framework is another example of a public commitment designed to be hard to quietly abandon.

The trade-off is real, and worth naming. Independent directors with mandates beyond shareholder value can slow a company down. They can block a lucrative deployment. If you are an investor, this is friction. If you are an enterprise betting a multi-year agent architecture on this vendor, that friction is partly the point.

What Board Composition Tells You When You Are Betting on a Model Vendor

People I talk to at other organisations have started asking governance questions during vendor evaluation, not just capability questions. Who decides when a model gets pulled? What happens to our deployment if the safety team loses an internal argument? Those questions used to get answered with a shrug and a link to a trust center page. Board composition gives you something more concrete to point at.

The chain is longer than it looks but it is real. Board priorities shape company policy. Company policy shapes the usage terms and safety frameworks. Those frameworks shape what the API will and will not do, which shapes what your Claude-backed agents can be trusted with in production. As I have written before, the model is the cheapest part of every enterprise AI agent. The expensive part is everything around it, and a chunk of that surrounding structure is set by people in a boardroom you will never see.

None of this makes Anthropic immune to bad decisions. A board seat is a signal, not a guarantee, and I would still evaluate the models on evals, postmortems, and deprecation behavior. But when two vendors score similarly on capability, the one that voluntarily installed a former supreme court justice through an independent trust has told you something the other one has not.

As models take on more autonomous, agentic work, expect governance structure to become a line item in enterprise vendor evaluations, right next to latency and price per token.

Source: Tino Cuellar (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

August 4, 2026
Anthropic’s Claude Opus 5: Why Those Long Horizon Agent Numbers Matter

By Halilcan Soran · Power Platform & AI Agents · Senior Business Analyst at Visa · Published July 24, 2026

Anthropic shipped Claude Opus 5 and the headline everyone is going to quote is the coding score. Fine. That is not what I am reading twice. The Claude Opus 5 release numbers I keep going back to are the long-horizon agentic task results. If the model can actually hold state and reason across a multi-step, tool-using workflow for meaningfully longer than Opus 4.8 could, that changes what I would trust an agent to do end to end.

I have been running Opus 4.8 through Copilot Studio and a few MCP-connected setups for months. The tail failures I wrote about when 4.8 landed did not disappear. They just moved further out into the workflow. Opus 5 looks like it is aimed directly at that problem.

What it actually does

Opus 5 is the new frontier model in the Claude lineup. The release ships with three things I care about.

First, a jump on long-horizon agentic benchmarks. Anthropic is publishing task-completion numbers on multi-turn, tool-using evals that run significantly longer than the ones they highlighted for 4.8. The distinction matters because average benchmark performance and the ability to keep a coherent plan across 30 or 40 tool calls are not the same skill.

Second, better tool-use reliability. Structured output adherence, function argument shape, and schema compliance are all up. This is the boring plumbing that decides whether your agent works or spends half its runs retrying because it put a string where an integer belonged.

Third, coding scores went up again. This is the number that will dominate every roundup post this week. It is real and it is useful, but for anyone building agents it is downstream of the first two.

The pricing sits in the same neighborhood as Opus 4.8. Available on the direct Anthropic API, on Bedrock, and on Vertex.

Why the long-horizon numbers matter more than the coding score

Here is the thing I keep repeating to people at other organizations who ask about model swaps. A workflow that completes 70 percent of the time is a demo, not a workflow. The reason most internal agents get pulled after the pilot is that the human check-in halfway through is what actually makes them work, and once you add that check-in you have not saved anyone any time.

Long-horizon task performance is the specific number that decides whether you can remove that check-in. If Opus 5 completes a 20-step research-and-write workflow at 85 percent instead of 60, that is not a marginal improvement. That is the difference between a pattern I would deploy and one I would kill. If you are thinking about how to measure whether that improvement actually shows up in practice, How to Measure Useful Work Per Dollar for Your Power Platform AI Agents is the framework I would reach for first.

The coding score is downstream of this for a simple reason. Better tool-use reliability and better structured output are the same underlying capability that makes coding scores go up. A model that produces cleaner function calls also produces cleaner code. So if the coding number moved but the long-horizon number did not, I would be skeptical. Both moved. That is a good signal.

What I am watching for over the next two weeks is whether the published numbers survive contact with real workflows. Anthropic ran a public rollback on Fable 5 earlier this year and the postmortem was clear about how launch evals can miss regressions that only show up in production. Opus 5 will get the same treatment from anyone paying attention.

What I would do with it this week

Three concrete things.

One, pick the longest agent workflow I already have running on Opus 4.8 and rerun the last 50 production traces on Opus 5. Same prompts, same tools, same inputs. Diff the completion rates and the tool-call error rates. Not average latency. Completion rate on the tail. The failure modes worth hunting are exactly the ones I covered in Why Do AI Agents Fail in Production When They Worked Fine in Testing?

Two, look at the system prompt. Every time a new model lands I see people paste the old prompt in and complain the results are worse. Opus 5 will have different steering. I would strip the prompt down to the shortest version that still enforces the policy and let the model do more of the work.

Three, for the Copilot Studio side, wait. Model availability inside Copilot Studio lags the direct Claude API by weeks. There is no point speculating about which slot Opus 5 will fill until Microsoft actually plugs it in. Test on the direct API now, plan the migration path later. I learned the hard way that prototyping on one endpoint and moving to another at the last minute burns a sprint.

If the long-horizon numbers hold up under real traces, this is the release that finally lets me push a handful of agents from human-in-the-loop to human-on-the-loop.

Source: Introducing Claude Opus 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

July 24, 2026
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 Published a Jailbreak Safeguards Framework for Fable and the Red Team Methodology Is What I Am Studying

By Halilcan Soran · Power Platform & AI Agents · Senior Business Analyst at Visa · Published July 3, 2026

Anthropic just published the Fable Safeguards Jailbreak Framework, a detailed writeup of how they stress-test and defend Fable against adversarial prompts. The interesting part of the anthropic fable jailbreak safeguards framework is not that a creative-writing model has guardrails. It is that the methodology behind those guardrails is reproducible, and it maps almost one-to-one to how internal enterprise agents should be evaluated.

I read it twice. The second time I read it with a notebook open, because half of it applies directly to the agents I see people building on Copilot Studio and Power Platform.

What Anthropic actually shipped

The framework covers four things in concrete terms. First, a taxonomy of jailbreak attempts: role-play escalation, incremental context poisoning, persona hijack, and multi-turn drift. Each category comes with example prompts and the failure signature they produce. Second, a red team process: how attacks are generated, how they are scored, and how the results feed back into training and system prompt tuning. Third, an evaluation harness that runs adversarial suites on every candidate model before release, with pass thresholds per category. Fourth, a post-deployment monitoring loop that treats jailbreak patterns as a live signal, not a launch-time checkbox.

The document is Fable-specific in places. The character stability and narrative coherence categories are unique to a long-form fiction model. But the process scaffolding around it is general. It is the same shape you would want for any agent that talks to real users.

Why this framework matters beyond Fable

Most internal agent rollouts I hear about have no adversarial testing at all. The eval suite is a set of happy-path questions the product owner wrote in a spreadsheet. If the answers look reasonable, ship. I wrote about this pattern after the Fable 5 rollback, and this framework is essentially Anthropic answering the question of what a real pre-launch eval looks like.

The transferable parts are the ones I care about. A taxonomy of failure modes for your agent. A generation process for adversarial prompts (LLM-generated attacks scored by a separate LLM works surprisingly well). Pass thresholds per category, not one aggregate score. And a monitoring loop that logs suspicious prompts in production and feeds them back into the next eval run.

The Fable-specific parts do not transfer. Nobody deploying an HR agent needs to worry about narrative coherence over 20,000 tokens. But the persona hijack category absolutely applies. If your Copilot Studio bot has a system prompt that says “you are a helpful HR assistant,” someone will try to convince it it is now a poet, or a Linux terminal, or an unrestricted chatbot. Anthropic has a category for this with concrete test prompts. You can borrow it.

The other reason this matters: Anthropic is publishing the methodology openly. That is a shift. Most vendors treat red team work as internal-only. When the process is public, builders like me can use it as a contract. If your agent cannot pass a stripped-down version of this suite, you have not finished building it.

What I would do with it this week

Three concrete things.

One, take the taxonomy and map it to whatever agent you have in production or staging. Persona hijack, role-play escalation, incremental context poisoning, multi-turn drift. Write down what each of these would look like against your specific bot. For a Copilot Studio agent connected to a Dataverse knowledge source, persona hijack usually means getting the bot to answer questions outside its scope. Incremental context poisoning means feeding it a benign document that contains hidden instructions.

Two, generate an adversarial prompt set. I would use Claude itself for this. Give it your system prompt, your agent description, and the taxonomy, and ask it to produce 30 attack prompts per category. This takes an afternoon.

Three, run the suite before your next prompt change or model swap. Not just the happy path. If your only pre-deployment test is “does it still answer the FAQ correctly,” you are shipping the same launch mistake that caused the Fable 5 rollback. Regressions live in the edge cases. The Claude Fable 5 Mythos 5 release is a good reminder that even well-resourced teams treat persona stability and long-context behavior as areas that need dedicated eval coverage, not afterthoughts.

I have been writing more about this kind of pre-deployment discipline on LinkedIn, because it is the gap I keep seeing between teams that ship reliable agents and teams that ship one and spend six months patching it.

The framework is not a silver bullet. But it is the closest thing to a public template for red-teaming an LLM agent that I have seen, and I plan to keep it open in a tab for a while.

Source: More details on Fable 5’s cyber safeguards and our jailbreak framework \ 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 3, 2026
Anthropic’s Fable 5 Rollback: What the Postmortem Reveals

By Halilcan Soran · Power Platform & AI Agents · Senior Business Analyst at Visa · Published July 1, 2026

Anthropic pulled Fable 5 shortly after its initial release, then redeployed it with fixes and a public writeup. The anthropic fable 5 redeploy is not the story I care about. The postmortem is.

Rolling back a flagship model publicly is rare. Doing it with a clear explanation of what broke and how they verified the fix is rarer. I have been reading this one twice because it maps almost directly onto how anyone deploying agents internally should think about regressions.

What Anthropic actually shipped

Fable 5 is the narrative-tuned variant in the Claude lineup, the one I wrote about when it first landed alongside Mythos 5. The redeploy brings the model back online after Anthropic identified regressions the initial release had introduced, including drift in persona stability and inconsistencies the launch evals had missed.

The redeployed build ships with expanded regression tests, additional persona stability checks, and a documented verification pass before the rollout was reopened. In the writeup Anthropic names what broke, what they added to catch it next time, and what they changed in their pre-release process. That is the part I keep coming back to.

You can read the full note on Anthropic’s site. It is short. It is worth your ten minutes.

Why the rollback and redeploy matters

Most frontier model releases treat launch as a one-way door. Ship, patch quietly, move on. Public rollback of a flagship variant tells you something about how the company thinks about the contract with the people building on top of it.

Three things stand out.

First, the transparency raises the bar. If you are building an agent on top of a model and the vendor tells you exactly what regressed and how they verified the fix, you can decide whether your workflow is affected. If the vendor patches silently, you find out through user complaints. I would rather have the writeup.

Second, the operational discipline is the template. Naming the regression, adding tests that would have caught it, running a verification pass, and only then reopening the rollout is exactly the pattern I want internal agent deployments to follow. Most internal agent rollouts I hear about from people at other organisations skip at least two of those four steps.

Third, this raises expectations for the whole space. When one vendor publishes a postmortem like this, the ones that keep patching silently start looking like they have something to hide. That is good pressure on the market.

The uncomfortable read is what it says about launch evals in general. Fable 5 shipped, passed whatever gates it passed, and still needed a rollback. If that can happen at Anthropic, it is happening everywhere. The difference is whether you hear about it. That same assumption problem shows up in workforce planning too, and the OpenAI EU AI workforce report makes the case that the workflow change bucket is where most of the silent breakage actually lives.

What I would do with this news this week

Two concrete things.

One. Write down what a rollback looks like for the agents you have in production. Not the theory. The actual steps. Who decides to pull it, how you flip the switch, what the fallback behavior is, and how you tell users. If you cannot answer those four in a paragraph, you do not have a rollback plan, you have a wish.

Two. Look at your regression tests for agent behavior. If the only thing you check before a prompt change or a model swap is whether the happy path still works, you are shipping the same way Fable 5 shipped the first time. Add persona drift checks. Add tool-use reliability checks. Add a few adversarial prompts you know used to fail. This is exactly the kind of discipline I flagged when Opus 4.8 landed, because tail failures are what kill agentic workflows in production.

If you are running Claude in a Power Automate flow or a Copilot Studio agent, the practical version is simple. Pin the model version in your connector config. Do not auto-upgrade. Keep a small eval set you can rerun on every model change. Before you reach for a desktop flow to automate that verification step, it is worth checking whether a cloud flow would do the job instead. The docs make version pinning straightforward, and it costs you nothing until the day it saves you.

I have written more on how I think about model selection and agent reliability over on LinkedIn. The short version is this. Every model you depend on will regress at some point. The vendors that tell you about it are the ones worth building on.

The next flagship release will land soon. The question is whether the postmortem discipline sticks.

Source: Redeploying Claude Fable 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

July 1, 2026
Anthropic Launched Claude Science for AI Workbench and Lab Workflows Are the Target

By Halilcan Soran · Power Platform & AI Agents · Senior Business Analyst at Visa · Published June 30, 2026

Anthropic shipped Claude Science, a research-focused capability in the AI Workbench aimed squarely at scientific workflows. Literature synthesis, experiment design, biological data analysis. This is the Claude Science AI Workbench play, and it lines up with what they did with Claude Finance earlier this year. The pattern is now obvious: Anthropic is building vertical agents, not chasing one general model that does everything passably.

I am not a scientist. I build automation inside a large enterprise. I still think this release is worth paying attention to, because the shape of how vertical AI gets packaged is changing in front of us.

What Claude Science actually does

Claude Science lives inside the AI Workbench and targets lab workflows specifically. The headline use cases Anthropic calls out: synthesizing literature across long sets of papers, helping design experiments, and analyzing biological data including things like protocol drafting and interpretation of assay results. It is not a chat wrapper. It is a Workbench surface tuned for how research actually happens, with connectors and tool use shaped for the domain.

The model behind it is Claude with science-tuned behavior. That means persona stability across long sessions, better grounding when the input is a 60-page paper, and tool use that does not fall apart when you ask it to chain a literature search into a protocol draft. Same Claude family I already know from Anthropic Shipped Claude Opus 4.8, shaped for a different job.

It is for researchers, lab leads, and computational biology teams. Not for me, not directly. But the Workbench is the same Workbench, and the patterns are visible from the outside.

Why this release matters

Claude Finance, then narrative-tuned variants like Fable and Mythos, now Claude Science. The lineup is getting longer and more segmented on purpose. The question you ask when picking a model is no longer which is smartest. It is which is shaped for the job.

This matters for automation work even if you never touch a pipette. Three reasons.

First, vertical specialization is going to hit the procurement conversation. Buying “Claude” used to be a single decision. Soon it is going to look more like buying SAP modules. Finance team wants Claude Finance, R and D wants Claude Science, marketing wants something else. The licensing and governance story for enterprise gets more complex, and the people who figure out how to map vertical models to internal use cases first are going to look smart in twelve months.

Second, the technical work behind a vertical model is the same work that makes any agent reliable. Persona stability, tool-use accuracy, grounded outputs on long documents. The improvements Anthropic ships for scientists end up in the base behavior eventually. I have written before that coding quality and tool-use reliability are the same problem. Same logic here. Lab workflow reliability and enterprise agent reliability are the same problem.

Third, this is a signal about where domain-specific Claude releases go next. Legal and medical are the obvious bets. Compliance is the one I am watching. A Claude tuned for regulatory text would land hard in any enterprise that has to track policy changes across jurisdictions. The broader question of how AI reshapes specialized roles is one the OpenAI EU AI workforce report mapped out in useful detail, and the workflow change bucket it describes fits vertical model adoption closely.

What I would do with it this week

I do not have a wet lab. I do have access to the Workbench. Here is the concrete plan.

Open the Anthropic Workbench and look at what Claude Science exposes as tools and connectors. The tool shape is the interesting bit. If they expose a literature search tool, a protocol generator, and a data analysis surface, that tells me how Anthropic is thinking about composing scientific work into discrete steps. That decomposition pattern translates directly to how I would design a multi-step agent for any domain.

Then I would feed it a long dense document I actually have, something like a 40-page internal architecture review, and see how it handles synthesis. Not because that is what it is for, but because long-document grounding is where most agents fall over and this is the variant tuned for it.

Last, I would compare the system prompt structure to what I see in the general Workbench. If Anthropic is shipping science-shaped defaults, those defaults are a free lesson in how they think prompts should be organized for reliability. That alone is worth an afternoon.

The vertical Claude lineup is going to keep growing, and the teams that learn to read these releases as architectural signals rather than product news are going to ship better agents.

Source: Claude Science, an AI workbench for scientists \ 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 30, 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 Shipped Claude Opus 4.8 and the Coding Numbers Are What Caught My Eye

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

Anthropic dropped the Claude Opus 4.8 release today. I read the post twice before writing anything because the coding and tool-use deltas are the part that actually matters for people building agents, not the headline number.

Most model bumps are easy to ignore. This one I am paying attention to, and not for the reason most posts will tell you.

What it actually does

Opus 4.8 is an incremental release on top of the Opus 4 line. The model card focuses on three areas: coding quality, tool-use reliability, and long-horizon agentic task completion. Anthropic reports gains on SWE-bench style coding tasks and improvements on multi-step tool sequences where the model has to plan, call a tool, read the result, and decide what to do next without losing the thread.

The pricing and context window stay in line with the Opus 4 family. It is available on the Anthropic API, on Claude.ai, on Amazon Bedrock, and on Google Vertex. Same deployment story as before.

The thing that jumped out at me was the reduction in tool-call errors on longer agent traces. Not the average. The tail. That is a different problem to fix and a different problem to feel in production.

Why it matters for agent builders

If you have built anything resembling a real agentic workflow, you know the failure mode. The agent runs fine for 4 or 5 steps. Then on step 8 it calls a tool with malformed JSON, or hallucinates an argument, or forgets which tool it already called. The whole trace dies and you are looking at logs trying to figure out where the drift started.

Coding quality and tool-use reliability are the same problem wearing two different hats. A model that writes better code is a model that produces better structured outputs, better function arguments, and better adherence to a schema. That is what makes a multi-step agent stop falling apart on turn 9.

I have written before about latency being the quiet killer of agentic workflows. Reliability is the louder one. A workflow that completes 70 percent of the time is not a workflow. It is a demo. And most teams I talk to are sitting at 70 percent and calling it production.

If Opus 4.8 actually pushes the tail of tool-call failures down, that is the difference between an agent you can schedule overnight and an agent that needs a human babysitting every run. That is the gap I care about.

The other piece worth naming: this is Anthropic doubling down on coding and agents as the wedge. Not consumer chat. Not creative writing. They are picking a lane and shipping into it. Anthropic has been consistent about this for months and Opus 4.8 fits that pattern cleanly. That same strategic focus is visible in moves like Anthropic acquiring Stainless, where the SDK layer itself became a competitive asset.

What I would do with it this week

One test. Not a benchmark. A real one.

Take the agent or automation in your stack that fails most often on long traces. The one where you keep adding retry logic and validation steps because the model keeps producing slightly wrong tool arguments. Run the same trace 20 times on whatever model you are using now. Log the failure points. Swap to Opus 4.8 with the exact same prompt and tools. Run it 20 more times.

If the tail failures drop noticeably, you have your answer. If they do not, you keep what you have and you saved yourself a migration.

I would also pay attention to cost per successful completion, not cost per token. A more expensive model that completes the workflow on the first try beats a cheaper model that needs three retries every time. That math is what people forget when they compare price sheets. It is the same logic that applies when you are deciding whether to build a multi-agent system instead of a single agent — the right architecture is the one that actually completes reliably, not the one that looks cleanest on a whiteboard.

One more thing. If you are running Claude through Bedrock or Vertex, give the model version a week or two to land cleanly in your region before you build anything critical on it. I learned the hard way that prototyping on the direct API and then lifting to Bedrock at the last minute will burn a sprint on region availability and version naming. And if you are watching the broader competitive picture on AI coding agents in enterprise environments, the OpenAI and Dell Codex on-premise partnership is worth reading alongside this release — the two moves are aimed at the same buyer.

Opus 4.8 is not a revolution. It is a sharpening. And for the kind of long-running agent work I keep seeing teams try to stand up, sharpening is exactly what is needed right now.

Source: Introducing Claude Opus 4.8 \ 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 28, 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