In other words, it is designed for companies to build on top of the Anthropic platform. Fo example, you are a SaaS and you want to build a way of running agents programatically for your customers, they basically offer a solution. It is not for personal use although you can certainly do so if you are prepared to pay the price for the API.
The downside is obviously this is locked to Anthropic models.
The other downsides is that the authentication story at the moment is underwhelming, hacking, and dare I say, insecure. I have a few reservations.
We already have this platform and I am putting together and open-source example how to create your own version of this.
Anthropic models are great but there are plenty of open-source models too and frankly agents do not need to run like claude code in order to be successful at whatever they need to do. The agent architecture entirely depends on the problem domain in my own experience.
We've got Claude Managed Agents, Claude Agent SDK, Claude API, Claude Code, Claude Platform, Claude Cowork, Claude Enterprise, and plain old 'Claude'. And honourable mention to Claude Haiku/Sonnet/Opus 4.{whatever} as yet another thing with the same prefix. I feel like it's about once a week I see a new announcement here on HN about some new agentic Claude whatever-it-is.
I have pretty much retreated in the face of this to 'just the API + `pi` + Claude Opus 4.{most recent minor release}', as a surface area I can understand.
To score a big IPO they need to be a platform, not just a token pipeline. Everything they’re doing signals they’re moving in this direction.
FWIW- IMO, being locked into a single model provider is a deal breaker.
This solution will distract a lot of folks and doom-lock them into Anthropic. That’ll probably be fine for small offices, but it is suicidal to get hooked into Anthropic’s way of doing things for anything complex. IME, you want to be able to compare different models and you end up managing them to your style. It’s a bit like cooking- where you may have greater affinity for certain flavors. You make selection tradeoffs on when to use a frontier model on design & planning vs something self hosted for simpler operations tasks.
The best performance I've gotten is by mixing agents from different companies. Unless there is a "winner take all" agent (I seriously doubt it, based on the dynamics and cost of collecting high quality RL data), I think the best orchestration systems are going to involve mixing agents.
Here, it's not about the planner, it's about the workers. Some agents are just better at certain things than others.
For instance, Opus 4.6 on max does not hold a candle to GPT 5.4 xhigh in terms of bug finding. It's just not even a comparison, iykyk.
Almost analogous to how diversity of thought can improve the robustness of the outcomes in real world teams. The same thing seems to be true in mixture-of-agent-distributions space.
Originally I thought they would stick towards being a model provider mainly, but with all the recent releases it seems they do want to provide more "services."
Wonder what part of the market 3rd party apps will build a moat around?
Call me stupid, but this sounds not like they want software developers to be around in a year or two.
I own a stake in a small brewery in Canada, and this feature just saved me setting up some infrastructure to "productionize" an agent we created to assist with ordering, invoicing, and government document creation.
I get paid in beer and vibes for projects like these, so the more I can ship these projects in the same place I prototype them the better.
(Also don't worry all, still have SF income to buy food for my family with)
Until then, every agent framework is completely reinvented every week due to new patterns and new models. evals, ReACT, DSPy, RLM, memory patterns, claws, dynamic context, sandbox strategies. It seems like locking in to a framework is a losing proposition for anyone trying to stay competitive. See also: LangChain trying to be the Next.js/Vercel of agents but everyone recommending building your own.
That said, Anthropic pulls a lot of weight owning the models themselves and probably an easier-to-use solution will get some adoption from those who are better served by going from nothing to something agentic, despite lock-in and the constant churn of model tech
(Not sure if it would be Sumerian, Esperanto or something more artificial. As long as it is esoteric enough for one company to hoard all the expertise in it.)
Having Opus write a spec, then send to Gemini to revise, back to Opus to fix, then to me to read and approve..
Send to a local model like Qwen3.5 to build, then off to Opus to review ...
This was such an amazing flow, until Anthropic decided to change their minds.
For Anthropic to have the best version of this software, they'd have to simultaneously ... well, have the best version of the software, but also beat every other AI company at all subtasks (like: technical writing, diagramming, bug finding -- they'd need to have the unequivocal "best model" in all categories).
Surely their version is not going to allow you to e.g. invoke Codex or what have you as part of their stack.
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Claude Platform
Date
April 8, 2026
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Today, we're launching Claude Managed Agents, a suite of composable APIs for building and deploying cloud-hosted agents at scale.
Until now, building agents meant spending development cycles on secure infrastructure, state management, permissioning, and reworking your agent loops for every model upgrade. Managed Agents pairs an agent harness tuned for performance with production infrastructure to go from prototype to launch in days rather than months.
Whether you're building single-task runners or complex multi-agent pipelines, you can focus on the user experience, not the operational overhead.
Managed Agents is available today in public beta on the Claude Platform.
Shipping a production agent requires sandboxed code execution, checkpointing, credential management, scoped permissions, and end-to-end tracing. That's months of infrastructure work before you ship anything users see.
Managed Agents handles the complexity. You define your agent's tasks, tools, and guardrails and we run it on our infrastructure. A built-in orchestration harness decides when to call tools, how to manage context, and how to recover from errors.
Managed Agents includes:
Trusted governance giving agents access to real systems with scoped permissions, identity management, and execution tracing built in.

Claude Managed Agents architecture
Claude models are built for agentic work. Managed Agents is purpose-built for Claude, enabling you to get better agent outcomes with less effort.
With Managed Agents, you define outcomes and success criteria, and Claude self-evaluates and iterates until it gets there (available in research preview, request access here). It also supports traditional prompt-and-response workflows when you want tighter control.
In internal testing around structured file generation, Managed Agents improved outcome task success by up to 10 points over a standard prompting loop, with the largest gains on the hardest problems.
Session tracing, integration analytics, and troubleshooting guidance are built directly into the Claude Console, so you can inspect every tool call, decision, and failure mode.
Teams are already shipping 10x faster with Managed Agents across a range of production use cases. Coding agents that read a codebase, plan a fix, and open a PR. Productivity agents that join a project, pick up tasks, and deliver work alongside the rest of the team. Finance and legal agents that process documents and extract what matters. In each case, shipping in days meant providing value to users faster.
“We want Notion to be the best place for teams to work with agents and get things done. We integrated Claude Managed Agents, which can handle long-running sessions, manage memory, and deliver high-quality outputs over time, to make that possible. Our users can now delegate open-ended, complex tasks, everything from coding to generating slides and spreadsheets, without ever leaving Notion.”
Eric Liu, Product Manager
“With Claude Managed Agents, our power users become like Galileo, contributing across domains far beyond a single specialty or discipline. We deploy each specialist agent within a week, managing long-running tasks across engineering, product, sales, marketing, and finance, generating apps, proposal decks, and spreadsheets in sandboxed environments. As agents become more capable, Managed Agents lets us scale safely without building agentic infrastructure ourselves, so we can focus entirely on democratizing innovation across the company.”
Yusuke Kaji, General Manager of AI for Business
“Claude Managed Agents dramatically accelerated our development of Asana AI Teammates — helping us ship advanced capabilities faster — and freeing us to focus on creating an enterprise-grade multiplayer user experience.”
Amritansh Raghav, CTO
“Before Claude Managed Agents, users would have to manually run LLMs in sandboxes, manage their lifecycle, equip them with appropriate tools, and oversee their execution, a process that could take weeks or months to set up. Now, with a few lines of code, users can spin up that same infrastructure at least 10x quicker than before. This opens up what's possible to be built by developers and vibe coders alike. We're going to see a surge of AI-native applications on web and mobile.”
Ansh Nanda, Co-founder
“Turns out telling developers what's wrong with their code isn't enough: they want you to fix it too. Customers can now go from Seer's root cause analysis straight to a Claude-powered agent that writes the fix and opens a PR. We chose Claude Managed Agents because it gives us a secure, fully managed agent runtime, allowing us to focus our efforts on building a seamless developer experience around the handoff. Managed Agents not only allowed us to build the initial integration in weeks instead of months, but has also eliminated the ongoing operational overhead of maintaining bespoke agent infrastructure.”
Indragie Karunaratne, Senior Director of Engineering, AI/ML
“Atlassian helps enterprises orchestrate work across humans and agents. With Claude Managed Agents, we can build agents for developers directly into the workflows teams already rely on in weeks instead of months, so customers can assign tasks right from Jira. Managed Agents handles the hard parts like sandboxing, sessions, and scoped permissions, which means our engineers can spend less time on infrastructure and more time building great features for our end users.”
Sanchan Saxena, SVP, Head of Product, Teamwork Collection
“Using Claude Managed Agents, we've built a system that can pull information from our users' documents and correspondence to answer any query they ask, even when we haven't built a specific tool to retrieve the data. Before Managed Agents, we would've had to anticipate every question our users might want to ask and build tools or prompt workflows for each one. Now, with Managed Agents it can code up any tool it needs on the fly, allowing it to handle virtually any user query. This cut development time by 10x, letting us focus on UX and integrating more data sources instead.”
Javed Qadrud-Din, CTO
“Claude Managed Agents made it 3x faster to build a production-ready meeting prep agent. We went from idea to shipping in a matter of days. Our agent researches every participant ahead of a meeting to surface what matters for moving the conversation forward. Custom tools let us feed in our own calendar and contacts data, MCP made it simple to connect external systems like meeting notetakers, CRMs, etc., and the managed harness handled the heavy lifting, including sandboxed execution and built-in web search. Letting us focus on building the product, not the infrastructure.”
John Han, Co-founder
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Managed Agents is priced on consumption. Standard Claude Platform token rates apply, plus $0.08 per session-hour for active runtime. See the docs for full pricing details.
Managed Agents is available now on the Claude Platform. Read our docs to learn more, head to the Claude Console, or use our new CLI to deploy your first agent.
Developers can also use the latest version of Claude Code and built-in claude-api Skill to build with Managed Agents. Just ask “start onboarding for managed agents in Claude API” to get started.
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That plus everyone is using 5 different vector DBs and reranking models from different vendors than the answer models etc.
quick question, how do you manage these side projects that kinda need to be production ready but aren't you are actual SF job lol?
some of these people think they are my actual customer/client but like i do it for fun and to help them out.
There's a lot of money to be made in small business automation right now.
1. We pay for saas, so we don't have to manage it. If you vibe-code or use these AI things, then you are managing it yourself.
2. Most Saas is like $20-$100/month/person for most Saas. For a software engineer, that maybe <1h of pay.
3. Most Saas require some sort of human in the loop to check for quality (at least sampling). No users would want to do that.
Number 2 is the biggest reason. It's $20 a month.... I'm not gonna replace that with anything.
Writing this message already costs more than $20 of my time.
I predict that the market will get bigger because people are more prone to automate the long-tail/last-mile stuff since they are able to
But beyond that, AWS is a very complex platform. Agents simplify saas, the agent itself manages the api calls, maybe the database queries, more of the logic. As software moves into the agent, you need less cloud capability, and a better agent harness/hosting. Essentially, this makes the AWS platform obsolete, most services make much less sense.
When the models have an off day, the workflows you’ve grown to depend upon fail. When you’re completely dependent on Anthropic for not only execution but troubleshooting- you’re doomed. You lose a whole day troubleshooting model performance variability when you should have just logged off and waited. These are very cognitively disruptive days.
Build in multi-model support- so your agents can modify routing if an observer discovers variability.
Which projects are standing out in this space right now?
> 2. Most Saas is like $20-$100/month/person for most Saas. For a software engineer, that maybe <1h of pay.
|Segment |Median Enterprise Price |
|--------------------------|------------------------------------------|
|Mid-market |~$175/user/month |
|Enterprise (<100 seats) |~$470/seat/month implied (~$47K ACV) |
|Enterprise (100-500 seats)|~$312–$1,560/seat/month range (~$156K ACV)|
Enterprise contracts almost always include a platform fee on top of per-seat costs (67% of contracts), plus professional services that add 12–18% of first-year revenue.So for a lot of companies, it's worth using AI to create a replacement.
I can see that, assuming models don't make some giant leap forward.
It works on top of k8s, so you can deploy and run in your own compute cluster. Right now it's focused only on coding tasks but I'm currently working on abstractions so you can similarly orchestrate large runs of any agentic workflow.