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Govern what your agents do, not just what they're asked.

Autonomous agents act on their own. The boundary they act through should be yours.

AI has moved from answering questions to taking actions - calling tools, reading data, spending money, and chaining steps with no human in the loop. Agent Control governs every one of those actions at runtime, binds it to an identity and a purpose, and writes it to the Ledger.

The shift

From answering to acting.

For two years the enterprise AI question was simple: what did the model say? A prompt went in, an answer came back, and governance meant inspecting that one exchange. Agents have changed the question. An agent doesn't just respond - it decides what to do next, calls a tool, reads the result, and keeps going until the task is done. One instruction can become a chain of dozens of actions that touch live systems and real data.

That is enormous leverage, and it is exactly why the old controls fall short. Reviewing the opening prompt tells you nothing about the refund the agent issued on step seven, the table it queried on step nine, or the email it sent on step twelve. The risk moved from the words a model produces to the actions an agent takes - and actions are what an enterprise actually has to answer for.

DataStrict treats the agent as what it is: an autonomous actor that needs an identity, a scope, and a boundary. Not a feature bolted onto a chatbot, but a first-class subject the Control Layer governs end to end.

The problem

A loop is harder to govern than a sentence.

An agent runs a loop - reason, act, observe, repeat. Each turn can invoke a tool, hit an API, or pull from a warehouse, and the next turn depends on what came back. There is no single chokepoint to inspect, because the consequential decisions happen in the middle of the run, not at the start.

So DataStrict governs the loop itself. Every tool call and every data read the agent makes passes through the Enforcement Fabric and is adjudicated against the agent's policy in the moment - allowed, redacted, blocked, or escalated. The agent keeps its autonomy for the work it's permitted to do, and loses it precisely where policy says it should.

The model

Four control points for every agent.

01

Identity

Every agent runs as a first-class identity - a named service account with a declared purpose, not an anonymous shared key. That identity travels with each action it takes, so the question 'who did this?' always has an answer.

02

Tool access

A deny-by-default allowlist of the tools and functions an agent may call. It can reach orders.read and kb.search; it can never invoke payments.refund or exports.* unless a policy explicitly grants it - per agent, per purpose.

03

Data reach

Row, column, and field scopes bound to the agent's purpose. A support agent sees the customer it is helping - not the whole table - and PII is redacted on the way back. The agent gets what the task needs and nothing more.

04

Action authority

Spend caps, rate limits, and escalation thresholds on consequential actions. A refund above a set amount, a bulk delete, an outbound email - these require a human or a model-graded review before they run, not after.

Identity

An agent is a subject, not a secret.

Most agents today authenticate with a shared API key and inherit whatever that key can do - which is usually far too much. Holding those credentials at the boundary is a secrets-management problem as much as an identity one: the moment an agent can act, actions trace back to a credential, not to a governed identity with a declared purpose.

Agent Control gives each agent a first-class identity bound to a service account, a purpose, and a policy. Its tool allowlist, its data scope, and its spend authority are all expressed as Edict - versioned, reviewed, and testable like any other code. Change the policy, and the agent's authority changes with it, everywhere, at once.

Because the identity is the subject of every decision, the same agent can be tightly scoped in production and run wide-open in a sandbox without touching a line of its application code. Governance lives in policy, not in the agent.

Multi-agent systems

More agents, more surface, same boundary.

The frontier is no longer one agent but many - a multi-agent system where a planner delegates to workers, agents call other agents, and swarms coordinate to finish a job. Each hand-off is a new place for scope to leak: a narrowly-permitted agent calling a broadly-permitted one, or a sub-agent inheriting authority nobody intended to grant it.

Because DataStrict enforces at the boundary rather than inside any single agent, it doesn't matter how the work is decomposed. Every agent - planner, worker, or tool-caller - acts through the same Fabric and is held to its own identity and policy. Authority does not silently flow downhill; each actor is adjudicated on its own terms, and the full chain of who-asked-whom is in the Ledger, every record signed with post-quantum cryptography so the trace stays trustworthy for as long as you retain it.

That is the difference between governing an agent and governing an agentic system. The second only works if the boundary is independent of the agents themselves.

The system

Agent Control is the Control Layer, applied to agents.

Nothing here is a bolt-on. The same primitives that govern prompts and responses govern agent actions - which is why agentic governance is something we already do, not something we had to invent.

Enforcement Fabric

The runtime that intercepts every agent tool call and output and applies the decision inline.

Edict

The policy language that expresses an agent's identity, tool allowlist, data scope, and spend caps as code.

MCP Gateway

How agents reach external tools and data - gated, scoped, and inspected through the Model Context Protocol.

Ledger

The append-only record of every step an agent took, the verdict, and the policy version that decided it.

FAQ

Agent governance, answered.

What is an AI agent, and why does it need its own governance?
An agent is an AI system that does more than answer - it plans, calls tools, reads data, and takes actions over multiple steps to complete a task on its own. That autonomy is exactly what makes it risky: a single request can fan out into dozens of tool calls and data reads that no human reviewed. Governing the opening prompt is no longer enough; you have to govern what the agent does.
How is this different from governing a chatbot prompt?
A chatbot has one input and one output. An agent runs a loop - reason, act, observe, repeat - so the surface to govern is every tool call and every data access along the way, not just the first message. Agent Control evaluates each of those steps against policy as it happens, rather than inspecting a single round trip.
Do you sit between the agent and its tools?
Yes. The Enforcement Fabric is on the data path, so an agent's tool calls and retrievals pass through it before they execute. A call that isn't on the agent's allowlist, exceeds its spend cap, or reaches data outside its scope is blocked or escalated before anything happens - deny-by-default and fail-closed.
What happens when an agent tries something outside its policy?
The action is stopped at the boundary and the outcome is one of allow, redact, block, or escalate. High-impact actions can be routed to a human or to a model-graded review instead of being silently denied, so the agent can still complete legitimate work while consequential steps get oversight.
Does this work with agent frameworks and MCP?
It's framework-agnostic. Because enforcement happens at the boundary rather than inside any one SDK, it works in front of agents built on common frameworks and orchestrators, and it gates tool and data access exposed over the Model Context Protocol. Your governance does not change when you switch frameworks.
Can we see exactly what an agent did?
Every step - the tool called, the data touched, the verdict, and the policy version that decided it - is written to the append-only Ledger as the agent runs. The full trace is queryable evidence, produced as a side effect of enforcement rather than reconstructed from scattered logs later.

Govern AI like infrastructure.

Talk to our team about deploying DataStrict across your enterprise stack.

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