Contextus is the action-aware control point for AI agent workflows. It prepares useful context, checks agent identity, routes model calls through policy, pauses risky actions for approval, and records proof.
Built for AI engineering, platform, and developer-tool teams using coding agents, MCP, CLIs, SDKs, and internal automation.
One governed path
Agents / IDE / MCP
Contextus Gateway
Context and action control in one workflow
Tools / APIs / files / commands / data
One control point
AI agents can touch repositories, commands, APIs, credentials, and production data. Contextus gives teams one place to prepare the working context, identify the actor, apply policy, request approval, and retain the decision trail.
Prepare the context. Identify the agent and tool. Apply managed policy. Hold risky work for review. Keep the decision and outcome together.
Works with existing workflows
How Contextus works
Follow one action from useful context through policy, human review, and a proof-ready decision record.
Working context gathered
Risk classified
Approval required
Audit entry written
1. Compile
Pull the files, docs, and task history that actually matter before the agent acts.
2. Govern
Policy: Requires human approval.
3. Approve
The agent wants to delete production data. This action is blocked until reviewed.
4. Prove
Set tool access, risk thresholds, routing preferences, redaction, approval rules, proof requirements, and Agent Passport scopes in one managed control layer.
Send routine approval decisions where teams already work while Contextus retains the policy result, reviewer, rationale, audit event, and proof trail.
Governed economics
Routine work can move to a lower-cost model path when policy allows. Sensitive and high-risk work stays on the approved path, and each decision can retain the model, policy reason, session, cost, and outcome.
Illustrative monthly scenario
See how routing routine work to a lower-cost model can change spend while sensitive and high-risk work stays on the approved model path.
Estimated cost avoided
$450
Policy-routed spend
$1,800
Routine traffic routed
50%
High-risk model path
Approved model
Selected monthly volume
250M tokens
$450 avoided
Example uses Claude Opus 4.8 at $5 input / $25 output per million tokens and Claude Sonnet 4.7 at $3 input / $15 output, with an 80% input / 20% output mix. Half of traffic is treated as routine. Opus 4.8 Fast Mode at $10 input / $50 output is not used in this graph. This is an illustrative scenario, not customer performance or guaranteed savings.
$450 requested / $360 policy-routed
$900 requested / $720 policy-routed
$2,250 requested / $1,800 policy-routed
$4,500 requested / $3,600 policy-routed
$9,000 requested / $7,200 policy-routed
Use stronger approved models for sensitive work and lower-cost paths for routine work.
Connect requested model, selected model, policy reason, session, and cost metadata.
Link routing evidence to the final tool action, approval, actor, and outcome.
Where teams use Contextus
Coding-agent teams
Review file writes, package installs, migrations, network calls, and deployment commands before they affect customer-facing systems.
Platform and AI teams
Route routine work economically, keep sensitive work on approved paths, and connect every routing decision to the same workflow evidence.
Security and review teams
Use Agent Passport and Tool Passport signals to make activity attributable, then export evidence for customer, audit, or incident review.
Deployment paths
Standard Mode
Add governance to MCP, editor-native, CLI, SDK, and managed-model workflows.
Hybrid Mode
Refine missing context before expensive or risky work, then govern the action.
Recursive Lab
Invite-only research for self-hosted and open-weight workers with human-readable handoff.
Next steps
Start with one coding-agent workflow, define the risky actions that need review, and keep proof from the first governed run.
