Use Global Workspace Theory as a lens for coordinated agents. Design selection, shared state, evidence broadcasting, and authority boundaries without confusing a large context window with a complete coordination architecture.
Design agent feedback around goals, observations, bounded actions, verification, and correction. Use control-theory concepts to examine stability, observability, and the limits of automation.
Understand how rewards and feedback shape AI behavior. Separate reinforcement learning, human preferences, and persistent adaptation, then evaluate whether rewarded behavior actually achieves the intended outcome.
Build an evidence contract that keeps observations current, scoped, and traceable. Learn why confidence calibration and evidence qualification answer different questions, and how to use both in an operational workflow.
Decide which agent observations may become durable memory, procedures, or training data. Build qualification, evaluation, release, and revocation paths that preserve evidence without automatically approving the lesson.
Turn model recommendations into bounded, authorized actions. Define execution contracts, recheck approvals, reconcile uncertain outcomes, and test the full workflow before expanding an agent’s production authority.
Separate feedback collection from changes to memory, runbooks, and model weights. Govern each update with provenance, evaluation, release controls, and a practical way to withdraw a mistaken lesson.
Test the complete agent coordination loop under failure. Exercise duplicate delivery, interrupted execution, delayed evidence, and changed permissions while measuring task completion separately from control failures.
Separate a plausible AI answer from a verified claim and an authorized action. Use the Schrödinger’s cat analogy carefully, then apply evidence gates to infrastructure decisions.
Choose diagnostics that can change the next decision. Bound investigation costs, protect diagnostic access, and keep verified evidence separate from approval, execution, and confirmed service recovery.
Keep shared evidence, human approval, and execution authority distinct. Preserve provenance, bind permission to exact operations, and revalidate the boundary when an agent is ready to act.
Evaluate what an agent attempted, what executed, and what happened to the service. Keep control compliance, appropriate behavior, and verified outcomes separate in the scoring and release decision.
Prevent retries and corrective actions from amplifying an incident. Define retry ownership, finite budgets, stabilization rules, and reconciliation for actions whose outcomes remain unknown.
Verify the outcome of an agent action beyond the tool response. Separate acceptance, configuration, convergence, and service evidence, then test the conditions that could produce a false success report.
Design a bounded shared workspace for AI agents with versioned state, visible disagreement, and controlled updates. Separate current task state from recovery history and reusable memory.
Define AI rewards around verified service outcomes. Separate permissions, success, escalation, and efficiency so an improved metric cannot conceal missing evidence or poor operational behavior.
Design AI inference recovery around the complete approved service. Include models, retrieval, authorization, application state, capacity, interrupted requests, and tested degraded modes.
Contain compromised AI agents across identity, tools, data, memory, and downstream systems. Reconcile accepted actions, preserve evidence, and use explicit gates for a controlled return to service.
Rehearse agent behavior in controlled environments before granting production authority. Test realistic failures, policy conflicts, recovery, and repeated trials against the scope the agent will actually receive.
Decide which production experiences may influence future AI behavior. Qualify observations, route corrections to the right layer, evaluate changes, and control their promotion into durable knowledge or policy.
