situation-framing

Evidence-backed task plans are created by transforming coding-agent requests into a structured task frame that classifies the work, then selecting relevant memory commands to gather supporting facts before producing a YAML handoff artifact.

Degraded mode handling detects when memory capabilities are unavailable or incomplete, stopping the agent from drawing unsupported conclusions and providing explicit repair commands to restore evidence access.

Prompt injection is prevented through redaction checks and quarantine-aware handling that isolates suspicious evidence, ensuring the agent processes only validated inputs within strict JSON interaction boundaries.

JSON command orchestration frames bug fixes, features, refactors, investigations, docs updates, and deploy preparations by driving the agent to fetch readiness, context packs, and targeted search explanations using machine-readable JSON.

The best way is producing a compact YAML artifact that includes risk checks, evidence gaps, and nextAction fields, creating a self-contained handoff package suitable for agent-to-agent or human-to-agent implementation transfer.

Unsupported judgments occur when agents lack evidence-driven command selection; the solution requires enforcing strict interaction boundaries that mandate evidence gap reporting and prevent conclusions requiring missing facts.