AI Agent Governance

Your AI agent can call any tool it wants. Or only the ones you let it.

Unmeshed runs AI agents inside a governed workflow, not a black box. Every agent is bound to a fixed set of tools, every dollar of spend is tracked against a ceiling, and every decision it makes is logged. Nothing routes outside the workflow you defined.

MCP nativeModel agnosticFree tier includes 1,000 agent calls a month

Trusted by teams at leading organisations

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The Real Question

Does this step need a model's judgment, or a rule that runs the same way every time?

Most agent workflows lose control at the same point: a step that should have been a rule gets routed through the model instead. That's where token spend gets out of hand fast, and where behavior stops being predictable.

Needs AI
Reading an unstructured request and figuring out what's actually being asked
Researching across multiple sources and synthesizing an answer
Drafting a response in the right tone for the situation
Deciding which of several bound tools is the right one to call
Needs a Rule
Checking whether a request has already been handled
Enforcing per-agent spend ceilings before a call goes out
Deciding whether a draft is safe to send automatically or needs a human
Logging what ran, what it cost, and what tools it touched

Request Intake

One entry point, not five different scripts

Every request, whatever the source, comes in through a single workflow trigger. That's where the case ID gets assigned and the run history starts, before an agent ever gets involved.

  • RULE

    Validate the request and assign a stable case ID

  • RULE

    Log the request before any model call happens

  • RULE

    Route based on request type

Request Intake Path

Request in

webhook / API

Validate Fields

# intake_validate

valid
invalid

Assign Case ID

# case.create

Rejected

# reject_request

Run history starts

Human Review Path

Review routing

decision table

Auto-approved

low risk

Reviewer

# human_review_wait

Resume

# finalize_and_log

Human Review

The agent drafts. A rule decides who sees it next.

A decision table checks the agent's output against confidence, cost, or category thresholds. Routine, low-risk outputs go straight through. Anything else waits for a person, with the agent's full reasoning and tool calls attached, not just the final answer.

  • RULE

    Route based on confidence and risk thresholds

  • HUMAN

    Review flagged drafts with full context, not a blank screen

  • RULE

    Resume exactly where the workflow paused once reviewed

Spend Governance

A ceiling the agent can't spend past

Per-agent monthly spend ceilings run alongside the agent, not after the invoice arrives. When an agent nears its cap, Unmeshed can throttle it or automatically downgrade it to a cheaper model, so the workflow keeps running instead of failing outright.

  • RULE

    Check spend against the per-agent ceiling

  • RULE

    Auto-throttle or downgrade model tier near the cap

  • RULE

    Attach every run to a cost, not just an outcome

Spend Check Path

Spend check

# check_spend_ceiling

Full tier

within budget

Throttle

near the cap

Auto-downgrade

near the cap

Continue to agent

cost attached to every run

Agent Dispatch Path

bound tool boundary

Internal search

bound tool

Knowledge base

bound tool

Agent

# agent_dispatch

Tool call logged

everything else: blocked

Agent Dispatch

The agent can only reach what you bind to it

This is the part most agent frameworks don't do well. Unmeshed's agent step is explicitly scoped to a fixed tool set at definition time. If a tool isn't bound, the agent can't call it, not because of a prompt instruction, because the runtime won't let it.

  • AI

    Agent researches and reasons using only its bound tools

  • RULE

    Tool access is enforced by the runtime, not the prompt

  • RULE

    Every tool call is logged as it happens

How the Architecture Works

Every step in an Unmeshed workflow declares what it is: code, model, human, or decision, so the runtime knows what to charge for, what to retry, and what to audit.

This is what makes agent spend and agent behavior something you can actually govern, not just monitor after the fact.

MCP native

Speaks standard tool protocols out of the box

Model agnostic

Not locked to one provider

1,000 / mo

Agent calls included on the free tier

Connects to Your Stack

Works with the agent stack you already write code in.

Any MCP toolPython / JS / Go workersAny REST or GraphQL APIOpenAIClaudeGeminiPerplexityMistralGrokGroqOllamaAzure OpenAIAWS BedrockLangChainCrewAI

Inside the Workflow

Four pieces, one workflow.

Bound tool access

An agent can only call what's explicitly bound to it. Enforced by the runtime, not a prompt instruction.

Per-agent spend ceilings

Throttle or downgrade automatically before spend becomes a surprise.

Full run history

Every model call, tool call, and cost logged automatically. One place to read, not logs scattered across five systems.

Decision tables

The rules an agent shouldn't have to re-derive every run, encoded once and editable without a redeploy.

Answers to the Question Finance Asks Eventually

Spend visibility and access control, not a surprise invoice.

Spend visibility

Per-agent cost tracked against a ceiling, not discovered on an invoice.

Tool scoping

Agents reach only what they're bound to, nothing else.

Audit logging

Every action, every case, timestamped and searchable.

Model flexibility

Swap providers without re-architecting the workflow.

Frequently asked questions

Still have questions? Talk to us.

Put a ceiling on what your agents can do and spend.

We'll map your current agent workflow, show you where spend and access controls are missing, and have it running inside Unmeshed in weeks.

Free tier, no credit card required