Control what AI agents can actually do.

Authorize consequential agent actions in real time and produce verifiable evidence of those decisions.

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Security review kit See the proof

Apps
Agents
MCP Servers
Praesidia Praesidia
Authentication
Guardrails
Policies
  1. Refund €8,250
  2. Policy: over €500
  3. Approval required
  4. Evidence generated
MCP tools
SaaS & APIs
Data
Infrastructure

Illustrative. Holds apply in enforce mode; approving a held call is in every plan.

How it works

See a risky action stopped.

One agent, one MCP tool, one €8,250 refund — held until a person approves it, and the receipt it leaves behind.

Illustrative scenario*
  1. 1

    An agent turns up

    Invoice Agentowner: Finance Platform

    Seen in your OpenTelemetry traces, then linked to a registered identity and adopted into the asset inventory.

  2. 2

    Its connections are inventoried

    Customer databaseStripe MCPSlack

    Stripe MCP: 12 tools, each listed with what it does.

  3. 3

    One permission matters more than the rest

    stripe.refund — write · moves moneycustomer records — read

    What it can do, and to what, spelled out in plain words.

  4. 4

    The agent tries to use it

    REQUEST · agent invoice-agent · action stripe.refund · amount €8,250

  5. 5

    Praesidia evaluates the call

    Identity Valid

    Permission Allowed

    Policy Amount > €500

    Approval Missing

  6. 6

    The refund waits for a person

    Held for approval

    Refunds above €500 require human approval, so the call pauses instead of running.

    Held Waiting for Finance Platform

    Approved By a person, not the agent

    Executed The refund runs as approved

  7. 7

    The decision leaves a Decision receipt

    actorinvoice-agent
    actionstripe.refund
    amount8250 EUR
    policyrefund-approval v3 (amount > 500)
    decisionheld — human approval required
    reasonApproval required above €500
    approvalapproved · Finance Platform
    resultexecuted
    evidencesigned + hash-chained · VERIFIED

    Verify it yourself

The refund scenario, agent graph and decision record on this page are illustrative: the real rule shape, not customer data. A managed MCP record establishes observed dispatch and response; it does not independently prove completion of the external refund. Holding a call applies when the organization runs in enforce mode. Approving a held call is in every plan; multi-step approval workflows are Enterprise — compare plans.

Approvals queue with one pending request: an Agent Tool Step-Up raised by the Invoice Agent, described as "Approve refund_payment for agent Invoice Agent", status Pending, expiring 21 September 2026, with approve and reject actions on the row.
This is the part a person owns: the refund the agent was not allowed to approve for itself.
  1. Discover

    Find the AI agents you didn’t know were running.

    Agents surface from your OpenTelemetry traces, from the API and SDK you register them with, and from strangers that try to talk to the ones you already run. Agents with no owner are listed too — the first question every review asks.

    Every registered MCP server is inventoried tool by tool, and a tool definition that changes after you approved it is flagged as drift.

    Explore Agent Discovery

    Illustrative*

    Invoice Agent

    • Customer database
    • Stripe MCP
    • Slack

    12 tools

    stripe.refund

    write · moves money

    Invoice Agent

    Owner
    Finance Platform
    Connections
    3
    Tool permissions
    stripe.refund — writecustomer records — read
    Rule
    stripe.refund — approval required above €500
  2. Secure

    Decide exactly what every agent you connect can do.

    Enforce identity, per-tool permissions, argument conditions and approval policies before a sensitive action runs — then quarantine, suspend or revoke an agent in one move.

    Rules read the arguments, so the same tool can be allowed under €500 and held for a person above it. New policies run in observe mode first — the verdict is recorded without changing the outcome — until you turn enforcement on.

    Explore Runtime Security

    One tool call, evaluated
    REQUESTagent invoice-agent · action stripe.refund · amount €8,250
    1Identity verified — the agent’s own short-lived credential
    2Tool permitted for this connection
    3Rule matched: amount > 500
    BLOCKEDhuman approval required
  3. Prove

    Know exactly what happened — and prove it.

    Praesidia records the decisions it makes in an append-only, hash-chained trail, signed under the default configuration, naming the actor, the action, the argument that mattered, the rule that matched, and the decision. A signed record shows it was not changed afterwards. It does not show that every action was captured.

    Export a signed bundle for independent verification; CSV and JSON are reporting formats. Check the bundle without us: praesidia-verify is an independent offline verifier that re-walks every signature and the whole chain on your own machine.

    Explore Audit & Evidence

    Decision record
    The fields a held refund leaves behind · Illustrative*
    actorinvoice-agent
    actionstripe.refund
    policyrefund-approval v3 (amount > 500)
    decisionHeld for approval — human approval required
    signature VERIFIED
    Verified offline, record by record.
Enterprise trust

Built for the stack you run and the auditors you answer to.

Connect supported agent and model routes to Praesidia, apply controls on those routes, and review how they map to the frameworks your compliance team uses.

Review the Trust Center

Works with

  • OpenAI
  • Claude
  • LangGraph
  • MCP
  • A2A
  • OpenTelemetry

Building the agent yourself? Find the recipe for your framework.

Check us before you trust us: run the independent verifier, read the public changelog, browse the documentation, or open the integration catalog.

Framework mappings support your review; they do not constitute certification or a guarantee of compliance.

Proud member of

NVIDIA Inception Program
Guides

Read the reference before you pick a tool.

Six guides on securing, governing and auditing AI agents: what to require, how each control works and where it stops.

Browse all guides

Secure the runtime

  • AI agent security guideIdentity, authorization, guardrails, trust scoring and incident response for autonomous agents.
  • MCP security guideAuthentication, tool-level authorization, rate limits and a hardening checklist for MCP servers.
  • BYOK AI gateway guideWhat a bring-your-own-key LLM gateway should enforce, and how it differs from MCP and API gateways.

Govern and prove

Design partners

We are taking on a small number of design partners running agents in production. You work directly with the team and have a say in what gets built next.

Apply to become a design partner

Test one control. Inspect the decision.

Create a free workspace, run the synthetic rule sample, then choose a supported path to connect your agent.