AGENTIC AI GOVERNANCE & RISK MANAGEMENT
Agentic AI governance and risk management
We define what agents can do, when people must approve an action and how decisions are recorded for review.
- A proceed-vs-escalate line set explicitly, per use case
- Guardrails and approval gates wired into the runtime, not filed as policy
- Immutable audit on every action, reconstructable for compliance
Agent failures are rarely the model reasoning poorly.
They are the engineering and governance around it.
We assume these failure modes as the default rather than the exception, and design a specific countermeasure for each before an agent touches a production system.
Governance is not a policy document filed after launch. It is a runtime control.
Layered, so most risk is caught early and cheaply.
Cheap deterministic checks run first. Expensive judgment runs only where nuance demands it.
Each layer is wired into the agent's runtime and owned by a named team, so the controls hold when the system changes.
Least-privilege permissions
Each agent gets the narrowest access to tools and systems its job requires, and no more.
Layered guardrails
Fast rule-based and classifier checks first, then an LLM-as-judge only where the call is genuinely nuanced.
Pre-execution approval gates
On consequential actions the agent proposes, a policy or a person approves, then it acts.
Budgets and circuit-breakers
Step limits and per-task budgets so runaway behaviour stops before it costs anything.
Sandboxed tool execution
An agent cannot reach beyond its remit, even when it tries.
Trajectory-level evaluation
Tool-selection accuracy, task completion, cost, and latency, monitored continuously, not just at launch.
Immutable audit logging
100% of actions logged, so every decision is reconstructable for compliance and debugging.
Implemented with production-grade tooling: NeMo Guardrails, Guardrails AI, and Llama Guard for the guardrail layer; LangSmith, Langfuse, and Arize for evaluation and observability. Aligned to your risk framework and cloud.
Where the agent acts, and where it stops and asks.
We call it bounded autonomy. The line is set explicitly, per use case, from three inputs.
The line is not fixed forever. As an agent proves reliable against its evals, the threshold moves and more cases run unattended, autonomy earned on evidence, not assumed on optimism, and pulled back the same way.
What you get, and who owns it after.
A risk assessment
The target agent or process scored against every failure mode above, with the gaps named.
A defined autonomy line
The proceed-vs-escalate policy per use case, with the thresholds and rules behind it written down.
An implemented guardrail and approval layer
Wired into the agent's runtime and tested, not described in a slide.
Observability and audit
Trajectory tracing and immutable logging on every action the agent takes.
A governance operating model
Who owns the controls, who reviews the audit trail, and who is accountable for moving the autonomy line, so governance survives after we hand over.
Governance sets the limits the rest of the practice builds within.
Agentic AI
Custom agent development and multi-agent system design, built inside these limits.
AI Managed Services
The controls enforced day to day, with monitoring, evaluation, and cost ownership.
Dedicated Teams
The risk and platform specialists who run governance alongside your teams.
What risk and security leads ask us first.
Only where it should. The autonomy line is set so routine, reversible, high-confidence actions run unattended, and approval is reserved for the consequential ones. Well-designed governance speeds adoption, because it is what lets your risk team say yes at all.
We treat every tool and retrieval output as untrusted input and constrain what an agent may do with it: least-privilege permissions, approval gates on consequential actions, and guardrails that inspect content before it drives behaviour.
Yes, that is the point of bounded autonomy. As an agent demonstrates reliability against its trajectory evals, we move the confidence threshold and let more cases run unattended. Autonomy is earned on evidence, and it can be pulled back the same way.
Define exactly what your agents may do unattended. Then make that answer safe.
We'll set the line with your risk, security, and finance teams, and build the controls that hold it.