Sentinel Agent
How does fuzzy intent become a coherent agentic product design?
A startup defines an incident-investigation agent that gathers evidence, proposes action, and keeps consequential execution under human control.
About this example3 strengths · 3 limits
What this shows
- — Turns a broad "AI incident responder" idea into explicit responsibility boundaries.
- — Follows observable investigation and approved-action features across the graph.
- — Records pilot feedback without silently rewriting the intended product.
What this does not prove
- — This is a product-responsibility spec, not a prompt, model, or implementation design.
- — The example does not prove that the agent is safe or effective in production.
- — The published snapshot shows one coherent starting point, not automatic discovery or delivery.
Published snapshotRevision and context
Example spec
Published snapshotSentinel Agent
System overview · open
Help operations teams investigate incidents and execute approved remediation without surrendering human accountability.
Responsibilities inside Sentinel Agent
Read purpose and contract
Why
The initial idea was an autonomous incident responder, but autonomy is not one responsibility. Investigation, evidence, authority, approval, and audit must remain separately challengeable.
Why
This spec describes product behavior and responsibility. Models, prompts, orchestration frameworks, and infrastructure remain implementation choices.
No declared dependencies.
No feature directly crosses this responsibility.
No incoming dependencies.
- Which incident classes are safe enough for the first customer pilot?
The snapshot is public content. Your own hosted spec remains tenant-scoped and is authored by your coding agent through authenticated MCP tools.
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