AI Agent Governance
Your newest employees don't sleep, don't take vacation, and can take a thousand actions before a human notices something's wrong. Here's how to govern them like you mean it.
Agents get built fast. Governance usually doesn't keep up.
A team stands up an agent to solve a real problem, wires it into a few systems and an MCP server or two, and ships it. Nobody assigns it an owner, sets an expiry, or writes down what it's actually allowed to touch. Six months later there are a dozen of these, most companies can't produce a list of them on request, and every one is a credential someone could compromise.
Agent Lifecycle Management
Every agent identity gets provisioned deliberately: an owner, a defined purpose, a scope, and an expiry or renewal date, the same joiner-mover-leaver discipline applied to employees. When the task ends, the credential retires with it.
MCP & Tool Access Governance
The Model Context Protocol and similar tool-calling standards make it trivial to connect an agent to a dozen systems in an afternoon. We scope exactly which MCP servers and tools each agent can call, and log every call.
Agent Identity & Verification
Before an agent gets credentialed, we establish what it actually is: which team owns it, what runs it, and how its identity is attested when it authenticates. Verified provenance is what makes an audit trail trustworthy.
Agent-to-Agent Authentication
As agents start calling other agents, not just APIs, the access chain gets harder to reason about. We design authentication between agents so one compromised agent can't silently escalate through everything it touches.
Audit & Accountability
Every autonomous action should trace back to an accountable identity, and ultimately an accountable person. We build the logging and review cadence so "why did this agent do that" always has a real answer.
Least-Privilege by Default
The same principle that governs human access applies harder to agents: start from zero permissions and add only what the task requires. Broad, standing agent credentials are the most common finding in an initial assessment.
A scoped agent identity, in practice.
Every agent we help provision gets a written access scope, not a verbal understanding of what it's "supposed" to do. A simplified example, for an agent that only needs to read customer records and draft (not send) email:
// scoped, time-bound, owned — not "full access" { "agent_id": "support-triage-agent", "owner": "support-eng-team", "expires": "2026-11-01", "tools": [ { "server": "crm-mcp", "scope": "customers:read" }, { "server": "email-mcp", "scope": "drafts:write" } ], "max_calls_per_hour": 200, "review_cadence_days": 30 }
No standing "admin" role. No email-send permission it doesn't need. An owner who gets asked about it every 30 days, and an expiry so it can't quietly outlive the project.
This isn't a future problem.
Most organizations already run more non-human identities than human ones, and AI agents are the fastest-growing category inside that number. Unlike a static service account, an agent can reason about new situations and take action nobody explicitly scripted, which is exactly what makes an over-privileged agent identity more dangerous than an over-privileged human one: it will use whatever access it has, at machine speed, without asking whether it should.
The fix isn't to slow agents down. It's to give each one an identity as deliberate and reviewable as any privileged employee account, so autonomy and control aren't in tension.
Find out what agents are already running in your environment.
Most organizations are surprised by the answer. We'll help you find out, and tell you honestly what's worth fixing first.
Related reading: AI Agent Lifecycle Management, MCP and the New Access Control Problem, and Building Accountability Into Autonomous Systems. Unfamiliar with a term on this page? Check the glossary.