Agentic AI architecture.

It is the design of systems where a model does not merely answer but acts: plans work, calls tools, changes state, and produces consequences. The architectural question is not how capable the agent is but what it is permitted to do, who authorised that, and what record survives the action. In practice that means a declared set of capabilities per agent, a separation of the roles inside a workflow so no single component both decides and executes unchecked, and an evidence trail an auditor can read afterwards.

PEVG stands for planner, executor, verifier, generator. It splits agent work into four modules with four contracts: a planner that decomposes the task, an executor that calls tools, a verifier that checks the result, and a generator that writes the response. The industry picture of one model that reasons, acts, checks its own work and answers in a single unbroken breath is seductive and does not survive production, because a system that does everything in one step cannot be held accountable for any of it. Separation is not bureaucracy; it is the architecture of accountability. PEVG is specified in Prompt Systems and Agent Orchestration, Book 2 of the Full-Stack AI Engineering Series.

By making authority explicit rather than emergent. Each agent declares a capability contract stating what it may do, and the workflow itself is declared as an inspectable object binding each module to its contract and naming the boundaries the flow may not cross. A pattern you cannot inspect is a preference; a pattern you can inspect is a control. Beyond that sits tiered human review, where the tier is a property of the risk rather than of the reviewer availability: auto-approve, lightweight review, or full supervisory review, with the tier chosen by payment size, regulatory impact and confidence.

It means the loop is an orchestration layer, not a courtesy. Oversight is real when the reviewer has the information required to disagree, the authority to stop the workflow, and a record showing both were available. An approval step that approves everything is not oversight, it is a signature, and it reads as one under examination. Tiers should be declared per node with a default and a confidence threshold, so escalation is a system property rather than a judgement call made under time pressure.

They make it different rather than uniformly harder. A single large agent is simpler to draw and far harder to govern, because every decision happens inside one opaque step. Decomposing into roles adds interfaces, and interfaces are where controls can be attached: a contract at the boundary, a verification before a result is believed, a permission scoped to one executor rather than to the whole system. The cost is orchestration complexity. The return is that failure becomes attributable, which is the precondition for fixing it and for defending it.

The Model Context Protocol is how agents reach tools and data, which makes it a governance surface rather than plumbing: it is the membrane through which an institution decides what its agents are allowed to perceive. Authorisation, authentication, scoping and audit at that boundary determine what an agent can actually do, regardless of what its prompt says. That is covered on the MCP governance page.

Both, and the distinction is stated rather than blurred. The patterns are specified in the published series and set in a deliberately fictional institution, so AgentMesh is a reference architecture and not a deployed client product. The production proof is elsewhere: Maxim, iSystematic behavioural-intelligence layer for Claude, is built on this discipline. Client engagements that apply these patterns are confidential.