Building Governance Frameworks for AI Agents

There’s a meaningful difference between an AI tool that summarises a document and an AI agent that can retrieve documents, reason across them, draft a response, and send it — in sequence, with minimal human involvement. The first is a productivity tool. The second is a system that can take actions on behalf of your firm, using client data, in ways that may be difficult to fully audit after the fact.

Both are being used in consulting environments right now. The governance conversation hasn’t kept pace.

Why agents are different

A language model answering a single question is relatively contained. An agentic system is different in kind: it can chain decisions, call external tools, retrieve from live data sources, and produce outputs that feed downstream processes. Each step introduces risk that the previous step’s governance doesn’t cover.

This isn’t an argument against agents. They’re genuinely powerful for consulting workflows — research, competitive analysis, document synthesis, client knowledge management. It’s an argument that the governance model has to cover the whole workflow, not just the model at the centre of it.

Start with the boundary

Every agent needs a clear scope defined before it runs: what data sources it can access, what tools it can call, under what conditions it can proceed autonomously, when it needs to surface something to a human, and when it should stop entirely rather than continue.

“Ambiguous autonomy” is where most agent security problems live. If an agent can browse the internet and retrieve internal documents in the same workflow, that needs to be a deliberate, reviewed design decision — not an emergent behaviour you discover later.

Log decisions, not just outputs

Standard AI systems typically log inputs and outputs. Agentic workflows need more: which tools were called and when, what data was retrieved and from where, where in the chain a human reviewed anything, what the agent’s reasoning was at each decision point, and what triggered any exceptions or stops.

This isn’t about surveillance — it’s about explainability. If a client asks how a piece of analysis was produced, you need to be able to answer. If a partner needs to review AI-assisted work before it goes out the door, they need to trust the provenance of what they’re reviewing.

Human review should match actual risk

One common mistake is applying the same level of human oversight to everything — which either creates so much friction that people route around it, or creates false confidence that review is happening when it’s mostly rubber-stamping.

Better model: identify the actions in an agentic workflow that carry real consequences — client-facing outputs, recommendations that will drive decisions, anything irreversible — and put meaningful review there. Let the low-stakes work flow through with lighter monitoring. The goal is coverage where it matters, not uniform friction everywhere.

This is where AI governance is heading

As agents become more capable, the governance frameworks that consulting firms build now will either scale with them or won’t. The firms investing in proper agentic governance — boundary definition, decision logging, risk-tiered human review — are building something they can extend. The firms treating each agent as a one-off prototype are accumulating technical and compliance debt they’ll have to unwind later.