AIGPE is the discipline of engineering a deterministic governance harness into the agentic platform — so every agent path is identity-bound, grounded, reviewed, human-gated at the autonomy level you set, and provable. It is the governance counterpart to the Agentic Development Platform: where that platform gives agents productive paths, AIGPE makes those paths governed and provable. Readiness, not certification.
Agents are no longer just a tool in the editor; they are becoming actors across the whole software lifecycle. Platform teams responded by evolving the Internal Developer Platform into the Agentic Development Platform (ADP), and the lifecycle into the Agentic Software Development Lifecycle (ASDLC) (terms: Weave Intelligence). The load-bearing insight is one Cognita has made all along: the most common failure mode isn't a model that can't perform — it's a platform that can't govern probabilistic agents at scale. The platform's hardest job is the deterministic harness that makes a probabilistic agent safe to operate. That harness is governance.
A large language model is not deterministic, and Cognita never claims it is. What AIGPE makes deterministic is the governance harness around it — and that is exactly what a regulated team needs:
Deterministic governance around a probabilistic model — never a deterministic model.
The technical buyer's real question isn't “what's your framework?” — it's “where does your governance actually run?” The answer the field has converged on: governance has to execute at runtime — where the agent acts — or it isn't governance, it's a document. (N. Kenney: governance intent must be compiled into runtime controls that execute alongside the agent, not left in policy documents.) Your IDE, your frameworks, and your cloud get the agent to runtime; Cognita governs the agent's work and proves it — control-plane and origin-agnostic, integrating with your stack rather than replacing it.

AIGPE makes the governance pillars of the agent platform first-class — and provable:
A named principal and an accountable owner for every agent and path.
Grounded-or-dropped (every claim cited) plus a mandatory fabrication-and-overclaim review, on every run.
A hash-chained, tamper-evident record your reviewer verifies offline from the command line.
One evidence base, scored across AIUC-1, ISO 42001, the EU AI Act and NIST AI RMF.
Origin, licence and export-control screening for the model you run — diligence and documentation, not legal advice.
Per-run metering, so agent compute is governed alongside the rest of your spend.
As you move agents up the autonomy levels (four-levels framework: Weave Intelligence), the governance must rise with them — so your agents never run faster than your guardrails.
| Level | Human role | What the harness enforces |
|---|---|---|
| L1 · in the loop | Executor | Citations and provenance on every suggestion; a minimal harness. |
| L2 · on the loop | Validator | Deterministic gates on every agent output; the reviewer checks the evidence, not each diff. |
| L3 · orchestrator | Orchestrator | Rule-based promotion, exception-only human review, cost governance, a full audit trail. |
| L4 · constraint-setter | Constraint-setter | Autonomous within hard, codified guardrails; a decision record on every action; escalation boundaries. |
Start with the free readiness score, then read the field of work it's built on. Readiness, not certification — accredited auditors certify.