Tag: Anthropic

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).

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).