Everything a startup engineering team needs to run agents in production
Build, Observe, and Govern aren't three separate products bolted together — they're one runtime, so an agent you trace today is the same agent whose guardrails you set tomorrow, with nothing to keep in sync between tools.
One runtime, two ways to build on it
Mirov's canvas lets anyone on the team — not just the engineers who wrote the original prototype — sketch an agent as a sequence of triggers, tools, and decision points. Drag a node in, connect it to the next step, and the canvas compiles it into a real, typed agent definition behind the scenes.
If you'd rather write the agent directly, the same runtime is available as a code-first SDK in Python and TypeScript. Agents built either way are interchangeable: open a code-first agent on the canvas and it renders correctly, and a canvas-first agent exports as code you can read, review, and put in version control like anything else your team ships.
- Drag-and-drop canvas that exports to real, readable code
- Code-first SDK for Python and TypeScript with the same runtime underneath
- Native tool-calling for your APIs, databases, and internal services
- Versioned agents — roll back a bad prompt or tool change in one click
A trace for every decision an agent makes
Agents fail in ways that don't show up in a normal application log. A tool call three steps back returns something slightly malformed, the model quietly compensates for it, and five steps later the final output is wrong in a way that's nearly impossible to reconstruct after the fact — unless every step was captured as it happened.
Mirov traces every plan, tool call, memory read, and model response an agent produces, and lays it out as a timeline you can step through. Cost and latency are attached to each span, so a slow or expensive agent is as easy to diagnose as a broken one.
- Full trace of every LLM call, tool call, and memory read
- Cost and latency per span, rolled up per agent and per customer
- Replay any historical run against a new prompt or model before you ship it
- Export traces as evaluation datasets for regression testing
Guardrails that live at the runtime, not in a prompt
A system prompt that says "never refund more than $200 without approval" is a suggestion, not a guarantee — the model can be talked out of it, and often is. Mirov enforces limits at the runtime level instead: spend caps, approval thresholds, and tool allow-lists are checked before an action executes, regardless of what the model decided to do.
Platform and security teams set policy once, centrally, and it applies to every agent built on top of it — so the person building a new agent doesn't have to re-implement the same safety checks that already exist for every other agent in the organization.
- Spend limits and human-approval thresholds enforced at the runtime level
- Per-agent tool allow-lists, so a support agent can't reach your billing system
- A full, exportable audit log of every decision for compliance review
- Policy changes apply instantly across every agent built on the platform
Guardrails · refund-agent
Agents need to reach real systems, not a sandbox
An agent that can only talk to a demo API isn't useful in production. Mirov ships connectors for the databases, clouds, and internal services most startup and mid-size teams already run, plus an open tool-definition format for anything custom.
Talk to us about your stackPlatform FAQ
No. Mirov's SDK wraps around your existing agent code — most teams get traces and guardrails on an existing LangChain, LlamaIndex, or custom agent within an afternoon, without rewriting the agent logic itself.