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Connect your agent, editor, MCP workflow, CLI, or automation.
Control what developer agents see, do, and prove.
Contextus is the control layer between developer agents and the tools they use.
It prepares the context an agent needs, checks governed actions against policy, pauses risky work for approval, and records the decision and outcome.
Your agent does the work. Contextus controls the boundaries.
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Agents / IDE / MCP
Contextus control layer
Context, identity, policy, approval, and evidence
Compilecontext
Governactions
Approverisk
Proveoutcomes
From intent to impact, safely.
Tools / APIs / files / commands / data
See Contextus in action
See what happens when an agent crosses a boundary.
Follow a delete-production-records request from working context through policy, human review, and a reviewable proof record.
1Compile
Working context gathered
2Govern
Risk classified
3Approve
Approval required
4Prove
Proof record written
1. Compile
Working context gathered
Pull the files, docs, and task history that actually matter before the agent acts.
2. Govern
Risk classified
Action: delete production customer recordsCritical risk
Policy: Requires human approval.
3. Approve
Approval required
This action would delete production data and remains blocked until an authorized reviewer decides.
Action remains blocked until reviewed.
4. Prove
Proof record written
- Agent
- Migration Agent
- Decision
- Denied
- Reviewer
- Platform lead
- Reason
- Outside approved migration scope
The agent never reaches the production database unless policy and approval allow it.
Fits your existing stack
Keep the tools your team already uses.
Contextus sits between agents and the systems they act on. Your team keeps its existing IDEs, agents, MCP servers, APIs, and automation.
Use Contextus when agents can write files, run commands, call APIs, install packages, access credentials, deploy changes, or affect production-adjacent systems.
Repository writes
Shell commands
Deployments
API mutations
Network calls
Credential access
Fits existing workflows
Coding agentsEditorsMCP serversCLIsSDKsCI workflowsInternal automation
How you adopt it
Start with one governed agent workflow.
Connect one workflow, define the boundary, then let policy decide when work continues, pauses, or stops.
Choose control
Select which tools, resources, and actions need review.
Work normally
Safe actions continue without unnecessary interruption.
Review risk
Risky actions pause with enough context for a human decision.
Keep proof
The request, decision, rationale, and outcome stay together.
How decisions work
Safe work keeps moving. Risky work gets reviewed.
Contextus does not put an approval prompt in front of every action. Policy determines what can continue automatically, what needs review, and what should be blocked.
Agent requestDecisionWhy
Read repository documentationAllowRead-only action inside the approved scope
Install a project dependencyRequest approvalChanges the project dependency graph
Delete production recordsDenyOutside the agent's authorized scope
Where teams use Contextus
Different teams need control at different boundaries.
Coding-agent teams
Let agents work in real repositories with the right review points.
Review file writes, package installs, migrations, network calls, and deployment commands before they affect customer-facing systems.
Hold risky writes before execution
Keep context and action review together
Platform and AI teams
Apply shared policy across agents, tools, models, and environments.
Keep routine work moving, hold sensitive work on approved paths, and connect each decision to workflow evidence.
Apply shared model and tool policy
See route, cost, approval, and outcome
Security and review teams
See who acted, what they requested, why it was allowed, and what happened afterward.
Use Agent Passport and Tool Passport signals to make activity attributable, then export evidence for customer, audit, or incident review.
Identify the agent, owner, and tool
Export a proof-ready decision trail
Governed economics
Put model spend under policy, too.
Contextus can route routine work to lower-cost approved models while keeping sensitive, high-risk, or capability-intensive work on stronger approved paths.
Every routing decision can retain the requested model, selected model, policy reason, session, estimated cost, and outcome.
Policy decides more than whether an action runs. It can also decide where the work runs.
Illustrative monthly scenario
Requested model spend vs. policy-routed spend
See how routing routine work to a lower-cost approved model changes estimated spend while sensitive and high-risk work remains on the approved model path.
Illustrative scenario, not guaranteed savings.
Monthly volume
Estimated cost avoided
$450
Est. policy-routed spend
$1,800
Routine traffic routed
50%
High-risk model path
Approved model
Selected monthly volume
250M tokens
$450 avoided
Requested model path$2,250
Governed routing path$1,800
Requested model path Governed routing path
View calculation and assumptions
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.
50M tokens$90 avoided
$450 requested / $360 governed routing
100M tokens$180 avoided
$900 requested / $720 governed routing
250M tokens$450 avoided
$2,250 requested / $1,800 governed routing
500M tokens$900 avoided
$4,500 requested / $3,600 governed routing
1B tokens$1,800 avoided
$9,000 requested / $7,200 governed routing
Keep strong models where they matter
Sensitive, high-risk, or capability-intensive work can stay on stronger approved models.
Route routine work when policy allows
Lower-cost models can handle eligible work when they meet your configured quality, data, and latency requirements.
Keep the routing decision explainable
Retain the requested model, selected model, policy reason, session, cost evidence, and outcome.
Advanced capabilities
More control when your workflows need it.
Agent identity
Tie actions to an owner, scope, lifecycle, and approval authority.
Semantic context
Retrieve and assemble relevant working context for the task.
Model routing
Apply policy to provider, model, quality, latency, and cost.
Recursive Lab
Experimental context-refinement research for advanced workflows.
Trust and evidence
Every decision leaves evidence.
Contextus keeps the policy check, approval path, agent identity, and outcome connected so technical teams can review what happened later.
Policy before execution
Governed actions are evaluated before reaching the target system.
Human attribution
Approval decisions retain the reviewer and rationale.
Agent identity
Actions can be tied to an Agent Passport and owner.
Reviewable proof
Context references, policy results, decisions, and outcomes remain connected.
Next steps
Govern your first real agent action.
Keep the workflow you already use. Define the boundary that matters, run one governed action, and see the resulting decision and proof.