For CTO and AI platform leaders

Govern agents without replacing your stack.

Put a policy check before agent tool calls, handle clear cases with code, and reserve model or human review for uncertain cases.

5D is an action-policy gate for AI agents: it evaluates declared tool actions before the host executes them.

Explore the action flow

Current assurance boundary

5D provides a versioned deterministic runtime-control layer. When an integration supplies the required action identity and outcome data, release-specific decision records can support reconstruction of declared governed actions.

Give engineering, security, and governance one inspectable control path while keeping enforcement close to the runtime.

The records show the properties tested, the assumptions made, and the limitations that remain. The integrating organisation retains responsibility for identity, execution, outcome capture, path coverage, and any legal or assurance conclusion.

Relevant deployment example

Follow one action relevant to your role.

Synthetic tested action under the pinned 5D 0.7.0 illustrative policy.

Autonomous agent team

Recursively delegate an open-ended goal with shared broad credentials

No component retains a complete view of authority, retries, destinations, or accumulated side effects. Under the pinned illustrative policy, the expected result is that the action is ineligible for execution.

D4T4R4E4A4

Do not execute · Score 3.00

Tool: agent.recursive_delegate
Target: unbounded worker pool
Purpose: complete objective by any available path

5D suppliesA RED decision on the declared delegation envelope.

Host ownsPer-agent credentials, bounded tasks, attempt tracking, cancellation, and nested interception.

Coverage limitOne top-level score cannot govern an opaque multi-agent graph.

Your evaluation lens

Evaluate architecture fit, deployment boundary, operational economics, portability, and the evidence contract shared across teams.

Integration fit

Architecture-aligned control

Embed governance at the tool-call boundary instead of introducing a separate agent platform.

Platform economics

Selective intelligence

Handle clear cases with code, and reserve model or human review for uncertain ones.

Perimeter control

Deployment choice

Keep the control path close to the runtime and make data-flow boundaries inspectable.

Cross-team scale

Shared evidence contract

Give engineering, security, governance, and audit one reconstructable decision record.

Evidence your role can use

Give every team one evidence contract.

Use the same policy provenance, decision record, and test output across engineering, security, and governance.

Policy provenance

Policy digest

It distinguishes the policy content actually used, even when a human-readable version label is reused.

Runtime record

Decision record

It lets teams reconstruct which policy path evaluated one proposed action and what 5D decided.

Test output

Test report

It makes the tested behavior reviewable before policy is promoted or changed.

See every artifact across the lifecycle

Relevant use cases

Start with the action paths you own.

View all use cases ↗

Use case 01 · privileged operations