AEP
Governed execution
for AI-assisted software delivery
AEP sits between agents, operators, and the delivery systems they touch. It enforces policy before execution, preserves operator intent, and emits the evidence needed for traceable, auditable change.
AI can propose. It should not execute unchecked.
AI-assisted delivery is moving from drafting and analysis into direct interaction with repos, CI, workspaces, and internal systems. Most delivery environments were designed for human-operated change, not model-driven execution.
That creates a control gap: teams can generate more change with AI, but they still need a bounded way to decide what is allowed to happen, under what authority, with what evidence, and with what recovery path when things fail.
Repo and CI mutations that exceed intended scope
Weak traceability across decisions, actions, and resulting changes
Missing audit evidence for AI-assisted execution
Policy drift across environments and teams
Low operator trust in autonomous or semi-autonomous workflows
Built for teams adopting AI-assisted delivery where control matters
AEP is designed for organizations that want the benefits of AI-assisted software delivery without turning their delivery systems into an ungoverned execution surface.
What AEP Does
AEP governs execution, not inference
AI gateways help govern how models are called. AEP governs what happens after model output is produced and before that output can affect real systems.
Evaluates against policy
Evaluates actions against policy before mutation.
Preserves execution context
Preserves actor, session, mission, and workspace context.
Enforces runtime boundaries
Enforces runtime boundaries around governed execution.
Emits verifiable evidence
Emits telemetry, diagnostics, receipts, and audit evidence.
Universal surface contract
Supports agent-led, human-led, and portal-led workflows over the same runtime contract.
Governs post-inference
Governs what happens after model output is produced and before that output can affect real systems.
Where AEP sits
AEP is not a model router and it is not an internal developer platform. It is the governed runtime between AI-driven intent and system mutation.
How governed execution flows through AEP
An agent, operator, or portal initiates a governed action through an ingress surface such as CLI, API, or a future portal. AEP evaluates that action against runtime policy, execution context, and bounded runtime rules before allowing it to touch downstream systems.
The result is not just a decision. It is a legible execution story: what was requested, what was allowed, what happened, and what evidence was produced.
The Architecture of Runtime Enforcement
“An agent is trusted to propose. It is not trusted to execute directly.”
Every action leaves a legible trail
AEP doesn't just enforce policy. It produces a set of operator-inspectable artifacts for every governed action: decisions, receipts, telemetry, and evidence bundles that can be retained, audited, or surfaced in whatever observability stack you already operate.
Conceptual preview. The artifact formats above represent the intended operator-output contract for AEP. Specific field names, schema versions, and retention policies are subject to change ahead of general availability.
Technical Surfaces
One runtime, multiple ingress paths
Operators and agents can enter the governed runtime through a CLI surface. The syntax below is illustrative only and shows the kind of actor, intent, and receipt flow the boundary is designed to preserve.
# illustrative governed-run flow
$ governed-run start --actor operator --workflow repo-change
> execution context created. boundary evaluation pending.
# runtime evaluates intent before downstream execution
$ governed-run inspect --id run_01HXYZ...
> decision: ALLOW | receipt: issued | telemetry: emitted
> target: github pull-request | policy trace attached
# outputs remain inspectable to operators and portals
$ governed-run artifacts --id run_01HXYZ...
> policy_decision.json | execution_telemetry.json | receipt.json
These examples illustrate interface direction and operator-facing shape. They are intentionally conceptual and do not represent committed GA command syntax, endpoint names, or policy schemas.
Adopt AI-assisted delivery without losing control
If your team is moving from AI experimentation toward AI-assisted execution, AEP gives you a governed runtime boundary before those workflows touch the systems you rely on.