GDPR Data Purposellm
Validation prompt
Verify any access to personal data aligns with the stated processing purpose declared in the request context.
LLM Model
gpt-4o-mini
Action on Failure🛑 block
Enforce OnInput
AI Agent Deployment Platform
Go from pilot to production. Use ContextGate to build secure, governed and cost-optimised AI agent teams.
or
One governed workspace
Your team and your agent fleet, together
Humans and agents share the same connections, files, database, skills, and policies — one audit trail, one place to govern.
Shared Workspace
🧑💻
You
Claude Code
👤
Sarah
Teammate
🤖
Research Agent
Remote · Cloud
🤖
Sales Agent
Remote · Cloud
🤖
Ops Agent
Remote · Cloud
🧑💻
You
Claude Code
👤
Sarah
Teammate
↕
Shared Workspace
↕
🤖
Research Agent
Remote · Cloud
🤖
Sales Agent
Remote · Cloud
🤖
Ops Agent
Remote · Cloud
Without governance
Agents go wrong quietly.
Three failure modes that quietly accumulate before anyone notices.
Burn tokens
Toolboxes blow past 100k-token baselines. Costs balloon, nobody notices until the invoice arrives.
Leak data
PII reaches external LLMs in raw form. Tool calls write private info into shared logs.
Ship hallucinations
Agents hand wrong answers to customers and downstream tools, with no human in the loop.
With ContextGate
What ContextGate gets you.
Outcome
Reduce Token Cost
Toolbox curation, vendor-agnostic model swap, retry-on-warn — up to 10× lower bill.
Outcome
Increase Safety
PII redacted before external LLMs see it. Tool access gated. Every action logged.
Outcome
Improve Reliability
Reusable rules catch hallucinations, brand voice violations, business logic. Force retries with feedback.
Core Features
Everything you need to deploy agents safely, cheaply and reliably — without building it yourself.
01→02→03→04→05→06→
Private Data Protection
PII redaction with privacy-safe tool use — protect private information from external LLMs while still allowing agents to use tooling.
Behavioural Rules Engine
Govern agent behaviour with agent-to-agent enforcement. Reusable runtime rules force retries with feedback — hallucinations, brand voice, factual accuracy, business logic.
Tool Management & Gating
Block, allow, or require approval on tool calls. Curate which MCP tools each agent can reach — least privilege, smaller blast radius.
Plug Into Your Data Lake
In-process SQL gives agents auditable, repeatable math across your data — no LLM hallucination on numbers, no copying data out.
Audit Logs & Observability
Every agent decision logged with full context. Filter, search, export.
Continuous Agent Tuning
The workspace assistant runs continuous audits across the fleet — drift, off-allowlist tools, PII regressions — and tunes prompts and policies from run history when prompted.
Outcome · Reduce Token Cost
Cut AI agent costs by up to 10×
Four levers on the AI bill — without giving up PII redaction, policy enforcement, or audit.
Swap in lower-cost models
Route any governed proxy to DeepSeek-V3.1, open-source models on OpenRouter, or self-hosted — fraction of the per-token cost.
Shrink the context window
Toolboxes only ship the MCP tool definitions an agent actually needs. Prompt baselines drop from 100k+ tokens to a fraction.
Cap spend per workspace
Hard USD ceiling per workspace. When the cap is hit, new requests are rejected — no surprise overage.
Stay vendor-independent
Policies, audit, and PII redaction live in ContextGate. Switch model vendor without rebuilding governance.
Toolbox curation in action
Before · Raw MCP114,200 tokens
- Salesforce MCP (full suite)38,400
- GitHub MCP (867 tools)41,200
- Slack MCP12,800
- HubSpot MCP14,600
- Linear MCP7,200
Every call ships this whole context. Pay for it on every turn.
After · Curated toolbox3,650 tokens
- Salesforce: create_lead, update_opportunity1,800
- GitHub: create_issue, comment_on_pr1,400
- Slack: send_message450
Only the tools the agent actually needs. Same agent, smaller prompt.
−97%prompt baseline reduction, applied to every call this agent ever makes
Feature · Private Data Protection
Real data for tools. Redacted for LLMs.
Triggers
Chat
Webhook
Schedule
Context Gate
Client Data RedactionLLM Policy
PII RedactionUK_BANK_ACCOUNT, SORT_CODE, EMAIL
Governs the prompt & response
Model
OpenAI GPTWorks with any model
Instructions
You are a finance ops agent. Keep client accounts and meeting logs in sync across Salesforce and HubSpot.
Toolbox
Salesforce Write RulesTool Policy
Governance CheckNo bank details written to a CRM
CONNECTIONS
Salesforcewrite blocked
HubSpotcall allowed
Database8 tables
- 1TriggersThe agent is asked to add a client's bank account to Salesforce and log a meeting in HubSpot
- 2Context GateThe Client Data Redaction LLM policy strips the bank account from the prompt before the model sees it
- 3ModelThe model plans the work and issues the tool calls, working only from the redacted prompt
- 4ToolboxThe Salesforce Write Rules tool policy blocks the create-account call; the HubSpot call goes through
Finance Ops Agent
Governed Trigger byChatPublish a chat interface for your users, WebhookTriggered by external HTTP calls, ScheduleRuns automatically on a cron schedule, EventsReacts to new emails, file uploads, alerts
Feature · Behavioural Rules Engine
Your rules, enforced at runtime.
Upload your style guide, business logic, brand voice, or custom regulatory policies — ContextGate's assistant turns them into reusable rules that catch off-policy outputs and force the agent to retry with feedback.
Rules from docs
Upload your style guide, brand voice, business logic, or custom regulatory policies. The assistant generates runtime rules.
Auto-retry with feedback
When an output violates a rule, the agent re-runs against the same model with the policy feedback injected (up to 3 attempts).
Reusable across the fleet
Author once, apply to every agent. No per-agent rule rebuilding when you ship a new agent.
PII Redaction Rules
Select which PII types to detect and redact
Governance Checks (LLM-based)
LLM-powered content validation rules
Consent Verificationllm
Validation prompt
Reject requests when the upstream consent flag is missing or expired for the data subject in question.
LLM Model
gemini-2.5-flash
Action on Failure⚠️ warn
Enforce OnInput
Data Minimisationllm
Validation prompt
Block tool calls that request fields beyond the minimum needed for the agent’s stated task.
LLM Model
claude-haiku-4.5
Action on Failure🛑 block
Enforce OnOutput
Feature · Tool Management & Gating
Pick the tools. Gate every call.
Curate which apps each agent can reach from 2,000+ pre-built MCP connectors — then block, allow, or require approval on every tool call. Least privilege per agent, smaller blast radius.
0 apps
Secret Backends
GCP Secret Manager, AWS Secrets Manager, Azure Key Vault
Health Monitoring
Automatic health checks and status tracking
Auto-Discovery
Sync tools and resources from MCP servers automatically
1000+ Apps
Composio integration for third-party OAuth connections
Feature · Plug Into Your Data Lake
A shared brain for your business.
Every agent reads and writes to the same workspace database. Calculations run in SQL — auditable, reproducible, no hidden logic.
Auditable Calculations
Every number your AI produces comes from a SQL query you can inspect. No black-box formulas — just transparent, reviewable logic.
Agents Work Together
One agent pulls client data from HubSpot, another generates invoices from it. They share the same tables — no manual copy-pasting.
Version History
Automatic snapshots with time-travel restore. If an agent writes bad data, roll back to any previous point in seconds.
Database
Query and manage your workspace data
Version History
Query EditorDuckDB
-- Agent: "What did we invoice last quarter?"
SELECT client_name, SUM(amount) AS total
FROM invoices
WHERE created_at BETWEEN '2026-01-01' AND '2026-03-31'
GROUP BY client_name ORDER BY total DESC;
Results3 rows · 12ms
| client_name | total |
|---|---|
| Acme Corp | £42,500.00 |
| Bright & Co | £28,750.00 |
| Delta Services | £15,200.00 |
Plug into your existing data lake
Amazon S3
Query Parquet, CSV, and JSON files directly in your S3 buckets via SQL — no copy, no ETL.
Google Cloud Storage
Same in-process SQL across GCS-hosted datasets. Tables stay where they live.
Azure Blob Storage
Read tabular data straight from Azure containers — credentials governed by ContextGate, not your agent.
Turn it into charts
Charts and dashboards from any SQL result
Agents (or you) can generate charts directly from query results — bar, line, pie, time-series — and pin them to a workspace dashboard. Visualisations stay in sync with the underlying data; refresh and they update. No BI tool to wire up, no separate export step.
Feature · Audit Logs & Observability
Full visibility on every agent decision.
Monitor, filter, and audit every request in real time. Dashboards for key metrics, drill-downs into individual tool calls with full request/response details.
Total Requests
12,847
+12%
Blocked
234
1.8%
PII Redactions
1,203
-5%
Avg Latency
120ms
-8ms
Activity Over TimeLast 7 days
Passed Warned Blocked
Policy ActionsLast 24h
Allowed85%
Redacted10%
Blocked5%
Top Tools by UsageLast 24h
Recent Policy Actions3 new
Blocked bulk delete attempt
PII redacted in Slack tool payload
New toolbox "Analytics" created
Real-Time Metrics
Track request volume, policy actions, and response times across all your agents in one dashboard.
Audit Logs
Every request is logged with full context. Filter by user, tool, policy, status, and date range.
Instant Alerts
Get notified when policies block requests, rate limits approach, or anomalies are detected.
Feature · Continuous Agent Tuning
The agent supervisor governs your agents.
A workspace assistant runs continuous audits and remediates policy violations across every agent on a schedule. Ask it to analyse run history and tune prompts.
Agent Supervisor
Compliance audit · 18 agents
Triggered by audit_agents · Finished 12s ago
14Pass
4Fail
Missing: IBANMissing: Sort code
Missing: Person names
Violation: Model
Violation: Tools
GDPRHIPAAISO 42001
Continuous audits
Run policy checks across every agent on a schedule, on every config change, or on demand — without writing one-off scripts.
Catch violations early
Flag agents that fail any rule — new tools added, redactions disabled, non-allowlisted models — before an auditor or regulator does.
One-click remediation
The Agent Supervisor proposes the fix, links the policy gap to a remediation, and applies it once you approve — keeping a full audit trail.
Enterprise AI Agent Governance
Built for the teams that have to sign off on AI
Unlike AI governance tools that focus only on models or prompts, ContextGate governs the agent's tools, actions, data access, and audit trail — so every team that has a stake in AI deployment gets the controls and evidence they need.
For CIOs & AI Leaders
Scale AI without owning every incident
Centralized agent governance, posture management, and a single audit surface across business units.
See the CIO solution →For Risk & Compliance
Defensible evidence for every agent action
Tamper-evident audit logs, PII redaction at the boundary, and mappings to ISO 42001, GDPR, HIPAA, and SOX.
See the compliance solution →For CTOs & AI Platform Teams
One governance layer for every agent you ship
Policy-based agent access management, MCP tool brokering, and lifecycle controls — across every model vendor.
See the platform-team solution →FAQ
AI Agent Governance, Answered
The questions enterprise buyers, risk teams, and AI platform leads ask before deploying agents.
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).
Why do companies need AI agent governance?
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.
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 owns this missing layer.
What are rogue AI agents?
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.
How does ContextGate control what agents can do?
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.
How does ContextGate protect sensitive data?
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.
Does ContextGate support MCP and tool access?
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.
How does ContextGate reduce hallucinations?
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.
How does ContextGate help with compliance and audits?
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.
Is ContextGate model-agnostic?
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.
What is an AI agent governance framework?
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.
What is AI agent identity governance and identity management?
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.
What is AI agent lifecycle management?
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.
What is AI agent posture management?
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.
What is AI agent access management?
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.
How does ContextGate compare to other AI agent governance software, tools, and solutions?
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.
Get in Touch
Ready to govern your AI agents? Let us know about your use case and we'll help you get started.