Best Practices

AI Agent Governance Best Practices

Ten practices for governing AI agents in production. Each one closes a common class of incident.

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01

Default-deny every tool

Every agent starts with zero tool access. Add allowlist entries explicitly per agent. Most production agents need a handful of tools, not dozens.

Common pitfall

Teams that hand agents a full MCP server end up with agents calling tools nobody knew existed.

02

Redact at the agent boundary, not after

PII redaction should happen before a prompt leaves your perimeter. Once a payload reaches a vendor model, it is too late.

Common pitfall

Redacting in vendor logs is not redaction. Vendors retain raw payloads; your audit log will not.

03

Use entity-aware redactors, not regex

Modern PII redaction needs to handle UK sort codes, IBANs, MRNs, NHS numbers, and dozens of formats. Use Presidio or equivalent — not a regex grab-bag.

Common pitfall

A single regex for "SSN" misses many of the formats your real customer data contains.

04

Layer LLM checks on top of deterministic rules

Some policies are fuzzy: consent verification, data-purpose alignment, data minimisation. Use an LLM-as-judge to evaluate them and block on violation.

Common pitfall

Trying to encode "is this consent-valid?" as regex always produces false-negatives.

05

Require approval for destructive actions

Bulk deletes, financial transfers, mass writes — never autonomous. Require a workflow approval (human or programmatic) at the policy layer.

Common pitfall

An agent that "helpfully" cleans up test accounts will eventually delete real ones.

06

Give each agent its own identity

Agents act on their own credential, not the calling user. This makes audit attribution clean and revocation possible without affecting humans.

Common pitfall

When agents inherit user credentials, every audit log entry says "the user did it."

07

Make audit logs structured and queryable

Free-text logs are useless under audit. Every entry should be a structured record with tool, policy, redaction events, latency, tokens — ready to filter and ship to your SIEM.

Common pitfall

A regulator that asks "show me every agent that touched PII in Q3" cannot wait for you to grep.

08

Map logs to your regulatory framework

Each log entry should map to control IDs in GDPR, HIPAA, SOX, ISO 42001 — whichever applies. Build the mapping once at the platform layer, not per-agent.

Common pitfall

Compliance teams that have to rebuild the regulatory mapping for every new agent will revolt — rightly.

09

Run continuous fleet audits

A governed agent today is not a governed agent forever. Run scheduled audits across every agent to catch policy drift, new tools, model changes, redaction regressions.

Common pitfall

A non-allowlisted preview model swapped in by a developer at 4pm Friday is the kind of thing only an auditor or a regulator catches.

10

Stay model-agnostic

Your governance layer must not be tied to a single LLM provider. Switching models should not require rewriting policy logic.

Common pitfall

Vendor-specific governance pins you to a single provider and makes negotiation impossible.

FAQ

AI Agent Governance, Answered

The questions people ask before they connect their AI apps.

What is ContextGate?
ContextGate is one connection you add to Claude, ChatGPT, Cursor or any MCP app. Every tool call through it is checked against your policies, blocked or redacted when it shouldn't happen, and logged.
Which AI apps does it work with?
Claude on the web, the desktop app and Cowork, ChatGPT, Claude Code, Cursor, GitHub Copilot in VS Code, Codex, Gemini CLI and any other app that supports remote MCP servers. The Works with page has the steps for each, including Microsoft Copilot Studio.
Is ContextGate free?
Yes. ContextGate is free, and you do not need a card to sign up.
How does ContextGate control what agents can do?
Every tool call your AI apps make through ContextGate is checked against your workspace policies, before the tool runs and again before the AI app sees the result. A policy can block or warn on a call, redact personal data, or stop a call that carries a secret. You can also switch individual tools off.
How does ContextGate protect sensitive data?
Personal data in tool results is replaced with placeholders before the AI app sees it: 40 built-in types, from emails and phone numbers to UK sort codes and National Insurance numbers, plus your own patterns. When the AI app sends a placeholder back in a later call, the tool gets the real value. The activity log stores arguments as sent and results redacted. Calls whose arguments contain an API key or other secret are blocked, and the secret is kept out of the log. See the privacy policy for how we handle data.
Which rules does ContextGate help with?
It keeps the record and applies your policies, which helps you become compliant; you still make the judgements and file the reports. The rules page covers UK GDPR, the EU AI Act, customer security reviews and ISO 42001, and for UK financial services the FCA's incident and third-party reporting, SM&CR and Consumer Duty. Policy templates such as GDPR Strict, EU AI Act, UK FCA Consumer Duty, UK AML / KYC and PCI DSS Cardholder Data give you a starting point.
Does ContextGate support MCP and tool access?
Yes. ContextGate is an MCP server. Your AI app connects to it once, and it brokers every tool call to your connections (apps such as Gmail, GitHub, Salesforce and Slack, or any remote MCP server) with policy checks, redaction and logging.
What does ContextGate not see?
ContextGate sees only the tool calls your AI apps route through it. It does not see an app's built-in tools, your chat with the AI app, or the model's answers.
What is AI agent governance?
AI agent governance is the layer of controls, permissions, and audit logging that determines what an AI agent is allowed to see, which tools it can use, what actions it can take, and how every decision is recorded. It is distinct from model governance (which controls the LLM) and data governance (which controls the underlying data stores).
How is agent governance different from model governance?
Model governance controls the LLM — choice of provider, prompt filters, model-level safety. Agent governance controls what an agent built on top of that model is allowed to do — its tools, its data access, its actions, and its audit trail. ContextGate works at this layer, on the tool calls an agent makes.

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