ai-agent-case-studylisted

# AI Agent Case Study Treat the agent as a probabilistic collaborator operating inside a control system. Do not present it as a linear CRUD flow or a magical chat box. ## Start with evidence Request or inspect the architecture, tool and permission model, interaction states, approval rules, traces, action history, evals, incidents, metrics, research, screenshots, and decision records. If an artifact is missing, mark the gap. Never request or publish hidden chain-of-thought. Use user-visible rationale, action summaries, provenance, tool calls, diffs, logs, confidence signals, and outcomes. ## Apply SUPERVISE Read `references/supervise-framework.md`, then cover: 1. **System role and scope** 2. **Uncertainty model** 3. **Permissions and autonomy** 4. **Execution observability** 5. **Recovery and reversibility** 6. **Verification evidence** 7. **Intervention mechanics** 8. **Success telemetry** 9. **Evolution and learning** Use `assets/ai-agent-case-study-template.md` for a new draft. ## Required distinctions - Separate model behavior, orchestration behavior, interface behavior, and product outcome. - Separate designed safeguards from safeguards proven in execution. - Separate a confidence display from actual calibration evidence. - Separate system completion from user success. - Separate latency perception from raw model latency. - Show both the autonomous path and the human intervention path. ## Narrative order Lead with the supervisory problem and pivotal control de