How We Secured and
Deployed ADLC with P1.
Built. Tested. In Production. This isn't a framework diagram — this is how ProvenanceOne actually governs every stage of the Agentic AI Development Lifecycle. A capability walkthrough showing our control plane in action across the full pipeline.
Our Lifecycle. Our Control Plane.
This is how we've deployed the Agentic AI Development Lifecycle — with our control plane governing every stage.
Goal Definition & Intent Design
Humans define core objectives and the negative space.
We codify intent into machine-readable constraints at this stage — turning ambiguous goals into enforceable boundaries before any agent runs. This is already built and deployed.
PRD Build & Constraint Mapping
Product specs evolve; risk tolerances must be made explicit.
We map every requirement to specific risk tolerances and operational thresholds. Guardrails ship with the spec, not after launch — this is how our policy engine ingests product specifications.
Skill Authoring & Tool Governance
Agents select tools, prompts, and external services.
Our control plane governs the tool layer at this stage — authorising only vetted servers and blocking unauthorised connections at the protocol boundary. Every tool call is gated.
Agent Orchestration & Capability Scoping
Sub-agents are assigned discrete tasks across the workflow.
We enforce Least Privilege between agents at this stage — our control plane prevents a Monitor agent from ever touching Admin or IAM tools, even by accident. This runs in real time.
Autonomous Coding & Real-Time AI-BOM
Agents write and refactor code at machine speed.
We generate a real-time AI Bill of Materials at this stage — tracking provenance for every dependency and flagging hallucinated or slopsquatted packages before they enter the build.
Autonomous Testing & Verified Execution
Agents run test suites and validate their own changes.
ProvenanceOne acts as the independent validator at this stage — requiring multi-agent consensus before high-stakes code changes can be merged. No single agent can self-approve.
Manual Eval & Human-in-the-Loop
Humans steer via observability dashboards.
We trigger mandatory human escalation the moment agent confidence drops below threshold. Humans are in the loop when they need to be — not before, not after. This is policy-driven.
Deployment via Policy-as-Data
Code is ready to ship to production.
Our control plane enforces automated gate-checks at deployment. Code ships only if it satisfies every Policy-as-Data constraint — no manual sign-off chain required. This is live.
Monitoring & Runtime Behavioural Security
Live agents operate in production environments.
We provide continuous supervision in production — detecting Semantic Drift and anomalous tool-call patterns, with an instant Kill Switch when behaviour goes off-script. Always on.
Nine stages. One control plane. Every one built and deployed.
See how we govern every stage.
Walk through our deployment methodology with the team and see each enforcement layer operating across the full lifecycle.