Most agent systems were built to be capable. Pea was built to be accountable. There is a difference — and in high-stakes environments, that difference is everything.
The distinction matters. Frameworks give you components to assemble. Wrappers give you convenience over existing models. A runtime gives you a governed execution environment — one where every tool invocation, every retrieval, every decision exists inside a bounded, auditable, replayable structure.
Pea owns the lifecycle, the memory boundary, the policy gates, and the evidence trail. The agent operates inside that structure. The structure does not operate inside the agent.
"Turns prefill from loose prompt stuffing into a controlled state-management interface."
Pea treats each turn as a state transition. A thin versioned runtime signal block enters the prompt. Structured outputs return alongside user-facing text. Reducers — not the model alone — remain the canonical authority for committed state.
Pea separates hot working memory from governed retrieval from deep evidence storage. It scores episodes across examination, introspection, and time — not to grade itself, but to get better slowly and reliably rather than fast and unpredictably.
Its operator surfaces let you see not just what the agent did but why, with enough context to reconstruct any decision without forensic archaeology. Every action traceable. Every retrieval governed. Every outcome scored against prior episodes so the system compounds rather than drifts.
Pea is being built to decide what should stay hot, what should become reusable current state, what should be retrieved only on demand, and how prior episodes should influence future action. That is what turns an agent into an operational layer.
"Gets better slowly and reliably rather than fast and unpredictably."
Most agent frameworks are going to face a painful retrofit moment. The enterprise market is beginning to require AI governance in earnest — auditability, traceable decision custody, bounded capability execution, human oversight surfaces. Systems built without those properties will have to add them later. That is an expensive, trust-destroying process.
Pea will not have that problem. The governance is load-bearing architecture, not cladding. Every structural decision — the unified capability envelope, the operator surfaces, the memory boundary, the policy gates, the replay architecture — maps directly onto a formal control requirement.
Pea is being built against a control matrix aligned with ISO/IEC 27001 and ISO/IEC 42001 — the information security and AI management system standards that enterprise procurement teams and regulators will increasingly require. The architecture was designed this way from the start. The certification programme formalises what was already true.
"Designed, developed, and operated within certified management systems for security and AI governance."
Not a roadmap. Not a set of architecture documents. A running system -- with real schemas, real memory stores, real operator surfaces, and real governance primitives executing against real workloads.
Development is moving fast. What is named here reflects the system at near-goals rather than a version-stamped snapshot. The primitives in the left column are live. The right column is the active build frontier.
"Gets better slowly and reliably rather than fast and unpredictably."
That principle applies to the build too. Every addition lands behind a stable seam. Nothing built now will require dismantling when the next phase arrives.
Development velocity means this reflects near-goals rather than a precise point-in-time snapshot. The runtime primitives named here are at or approaching production readiness.
Not pilots. Not demos. Design partnerships — organisations who have already felt the gap between what current agent systems promise and what they deliver in production, and who want to shape the solution rather than wait for it.
If your team has hit the wall on traceability, memory integrity, or runtime control, that is exactly the problem Pea is being built to solve. The conversation starts with your problem, not our pitch.