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Every agent controlled. Every outcome trusted. Every cost known.

Assemble, govern and observe every agent from one place: its model, its tools, what it's allowed to do, and what it costs.

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Without
AI Foundry.

With
AI Foundry.

AI is a time machine: what used to take a year to build up now arrives in a week. Which makes one question worth answering early: which agents run in production, what can they touch, and what do they cost?

Problem

Around

35%

of developers use their top AI tools through personal accounts.

Solution

AI Foundry gives every agent a governed harness before it runs.

The model, the instructions, the tools and the skills, fixed in a Playbook.

Problem

82%

of enterprises have unknown AI agents running.

Solution

One place that knows every agent, the model it runs on and the tools it can call.

Problem

A prototype reaches production without ever meeting your security policy.

Solution

Policy enforcement and human checkpoints are part of the run, not a review after it.

Problem

A year of AI budget disappears in a few months, and nobody can say where.

Solution

Token spend and cost rolled up per agent, Playbook, team and model.

An overrun is traceable to its source before it becomes a line item nobody can explain.

AI Playbook: make your harness engineering easier.

Every agent runs inside a harness: the model it runs on, the instructions it follows, the tools and skills it can call. Configured on its own, an agent can reach for any of them, and nobody outside its owner can say which. A Playbook draws the frame.

What a Playbook fixes

  • Agentdoes the work, inside the frame
  • Modelwhich model it is allowed to run on
  • Instructionsthe brief it follows, written once
  • Skillsreusable capabilities, a level above a raw tool
  • Toolsauthorized MCP tools, from one registry
  • Guardrailswhat it may not do, and who signs off
  • Memoryshort term, long term and team-shared

AI Playbook

The governed harness around all of it. You engineer it once: it fixes each piece before the run starts, and AI Foundry enforces it on every run.

One agent, or several: sequential, parallel, or in a loop.

It follows you into the editor.Pull governed Playbooks into VS Code, JetBrains, Claude Code or Cursor, or use the CLI, and run them locally against real code. Two-way sync flags drift when the Foundry version changes.

Foundry

One registry for every agent, tool and model.

A Playbook can only draw on what AI Foundry knows about. The registry is what it knows: the agents that run, the people who own them, the tools and models they are allowed to reach.

Agent Registry & Identity

One inventory of the agents, assistants and custom models running across your teams, each with an owner, an identity and scoped access.

YOU GET

You can say which agents run in production, who owns them and what they are able to reach.

Tools, Skills & Spec Templates Registry

The pieces agents are built from, kept in one place and versioned: the tools they may call, the skills they reuse, the spec templates their output has to follow.

YOU GET

A capability written once is reused by every team, instead of rewritten per agent.

Model Library

The models an agent is allowed to run on, declared centrally. A Playbook points at the library, so changing a model is a change in one place.

YOU GET

You can move to a new model without reopening every agent that used the old one.

MCP Gateway

Every MCP server federated behind one governed endpoint, with agent authentication and authorization, traffic routing and rate limiting. Agents discover and call only the tools the registry authorizes for them.

YOU GET

One governed source of tools, not one connection to manage per server.

When one agent isn't enough.

A multi-agent Playbook is a graph of agents you draw on the Agent Graph canvas: orchestration nodes decide how the agents run, and the shared memory and the approval points are written into the same Playbook.

Chain agents so one's output becomes the next one's input, with an explicit contract at each step.Run agents concurrently on the same or split input, then merge results through a configurable join.Send the work back to an earlier agent until the check passes, with a configured maximum number of iterations so a run cannot spin forever.An agent can hand off control to another agent mid-task, passing a scoped payload.

Inside any of them

See what every agent and human actually did, and what it cost.

LLMOps: full tracing of every run, at every level. FinOps: cost tracked at the same granularity.

  • Full trace, every level

    A span for the overall run, for each agent step, for each tool/MCP call, and for each LLM call.

  • Rich detail per span

    Token usage, latency, model and provider, tool names and arguments, status and errors.

  • Trace waterfall in Foundry

    Drill into any span, see cost and latency per step.

  • FinOps, cost where you can act on it

    Token spend and cost rolled up per agent, Playbook, team or model, not just per run, so a budget overrun is traceable to its source before it becomes a line item nobody can explain.

  • Open export

    OTLP to any standard backend, Grafana, Jaeger, Tempo, or your existing APM.

Run a Playbook from where you already work.

Local runs from your IDE land in the same Foundry traces as server runs.

Fewer tokens, on work that holds up.

The measure that matters is not the cheapest run, it is the cost of a result you can use. AI Foundry works on both ends of it.

The same task on two identical lanes. On the first, the call is sent whole, the output leaves unchecked, the error surfaces later and the run starts over. On the second, some of the call's tiles stop before leaving, served from cache, retrieved on demand or stopped by the cap; an eval checkpoint sits before the output, and there is a single run.

Cheapest run

Everything sent, every time. Wrong, found later: run the whole thing again.

With AI Foundry

Cached, retrieved on demand, capped: not sent, not paid for.

Fewer tokens: what leaves on every call

  • Caps and model routing

    Budgets per team and use case, enforced on the call itself. Past the cap, a request is stopped or routed to a cheaper model, before the tokens are spent rather than after the invoice.

  • Prompt caching

    What an agent asks again and again is served from cache instead of being paid for again.

  • Dynamic context retrieval

    An agent receives the context the task needs, retrieved at run time, instead of carrying an oversized window into every call.

Work that holds up: what comes back

  • Eval checkpoints

    Automated checks on the output before it moves on, so a cheap run that produced the wrong thing is caught rather than paid for twice.

  • Optimized Playbooks

    A Playbook tuned once lifts every run that uses it: the same work, on fewer tokens, for every team that adopts it.

  • Background and hybrid workflows

    Autonomous agents run in the background, alongside deterministic services, so work scales out without a person waiting on each step.

Where this shows up.

Learn moreLearn more

Frequently Asked Questions

AI Foundry is where you assemble and govern the agents you run. An AI Playbook fixes each piece before a run starts: the model, the instructions, the skills, the tools and the guardrails. Registries hold the agents, tools and models, the MCP Gateway brokers their tool calls, and every run is traced down to its cost.

Agents don't run in isolation.

AI Foundry governs them. Here's what it plugs into, module by module.