Cut AI agent costs 3×.
Without sacrificing performance.

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).

Agents are not chatbots — they take actions, use tools, and access systems. Without governance, they can expose regulated data, execute unauthorized actions, hallucinate when they lack grounded data, and leave no defensible audit trail. No regulated company can deploy agents at scale without it.

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 owns this missing layer.

Rogue agents are AI agents that act without supervision — they access data they should not see, take actions they are not authorized to take, leave no records, and hallucinate when they lack the right data. Governance turns rogue agents into governed digital employees. See example governed agents for what this looks like in practice.

ContextGate enforces policy-based controls on every agent action: which MCP tools an agent can call, which data sources it can read, which workflows require approval, and which outputs are blocked or redacted. Policies are versioned and applied consistently across every model and connector.

ContextGate detects and redacts PII (emails, phone numbers, account numbers, SSNs, custom patterns) across inputs, tool payloads, model calls, and results — before sensitive data is exposed to a vendor model or stored in logs. See the privacy policy for how we handle data.

Yes. ContextGate is an MCP-native governance layer. Agents discover tools via MCP, and ContextGate brokers every tool call with policy checks, redaction, and audit logging — across 2,000+ pre-built connectors or any MCP server URL.

Hallucinations spike when agents cannot reach the right grounded information. ContextGate gives agents safe, governed access to company data via a zero-copy SQL engine — so they answer with real data instead of guessing — while keeping every retrieval under policy controls.

Every agent decision, tool call, redaction event, and policy outcome is logged with full context. Compliance teams get an evidence trail that maps to GDPR, HIPAA, SOX, and ISO 42001 controls — without the engineering team having to build custom logging.

Yes. ContextGate sits between your application and any LLM provider — OpenAI, Anthropic, Google, Azure OpenAI, open-source via Ollama, or your own. Switch models without rewriting your governance rules.

An AI agent governance framework is the set of policies, controls, and audit mechanisms that determine how autonomous AI agents behave inside an organization. It covers identity, permissions, data access, tool brokering, approvals, redaction, and a tamper-evident audit trail. ContextGate ships this framework as a runnable platform — policies are versioned in code, enforced at the proxy layer, and applied consistently across every model, tool, and connector.

AI agent identity governance is the practice of giving each agent its own verifiable identity — distinct from the human caller — and managing the full lifecycle of that identity (creation, scoping, rotation, revocation). ContextGate issues a unique identity per agent, attaches the policy bundle it runs under, and records every action against that identity in the audit log. This is how you answer "who did what" when an agent action is questioned.

AI agent lifecycle management covers everything from creating an agent (define its tools, data scope, policies) through promoting it to production, monitoring its behavior, updating its capabilities, and retiring it safely. ContextGate gives you per-agent versioning, environment promotion (dev → staging → prod), drift detection, and structured offboarding so a deprecated agent cannot keep acting.

AI agent posture management is the continuous assessment of how secure and compliant your agents are right now — what tools they can call, what data they can reach, which policies cover them, where redaction is enforced, and where gaps exist. ContextGate gives security and risk teams a live dashboard of every agent's posture so issues are caught before they become incidents.

AI agent access management is the access-control layer for AI agents: which tools they can invoke, which data sources they can read or write, which workflows require human approval, and which actions are always denied. ContextGate enforces these as policy-based controls at the proxy — default-deny, per-agent allowlists, row-level data scoping, and approvals for high-risk steps — so an agent physically cannot exceed the access it was granted.

Most AI governance tools focus on the LLM (model governance), the data store (data governance), or the retrieval index (retrieval governance). ContextGate is the only category that governs what an agent built on top of those layers is allowed to do: tool brokering via MCP, per-agent permissions, PII redaction at the boundary, approvals on high-risk actions, and a full audit trail. See the agent governance guide for a deeper comparison.