The question we're answering
“What is an agent allowed to do, and how do we prove what it did?” Autonomous agents introduce a new category of risk: actions taken without a human in the loop, tool permissions that expand quietly over time, and no reliable record of what an agent actually did and why.
Who this is for
Organizations building or deploying AI agents with real tool access, from internal automation to customer-facing agents, who need permission boundaries and an audit trail before an agent's mistake becomes an incident.
What this covers
Agent security is a distinct discipline from general AI governance. This engagement covers:
- Tool permissions: exactly what actions and systems each agent can reach
- Identity and authorization: how an agent's actions are attributed and constrained
- Human approval: which agent actions require a person to sign off before they execute
- Logging: a reliable, tamper-resistant record of what an agent did and why
- Testing: adversarial testing of what an agent can be manipulated into doing
Outcomes & deliverables
- A documented permission model for every agent with real tool access
- Human-approval checkpoints on consequential or irreversible actions
- A logging and audit trail that survives scrutiny
- Tested boundaries, not assumed ones
How we approach it
Catalog every agent in use and the tools/systems each one can reach.
Adversarial testing of permission and approval boundaries.
Tighten permissions, add approval gates, and build the audit trail.
Regulatory readiness
Governance work here feeds directly into regulatory evidence, not just internal policy.
Industries & use cases
Proof
50 active enterprise clients, 100+ SMB clients, and 1,000+ assessments delivered per year: this isn't our first engagement like yours.
Expert reviewer
