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Agentic AI startupGreenfield product13 nodes
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.
Example spec
Published snapshotFive human lenses over one authoritative committed specification.
13 nodes · 2 features · 3 criteria
What is the system made of?
componentopen
Sentinel Agent
Help operations teams investigate incidents and execute approved remediation without surrendering human accountability.
7 responsibilities in scope2 feature paths0 external dependencies
Responsibilities inside Sentinel Agent
Open questions
- Which incident classes are safe enough for the first customer pilot?
Immutable public snapshot10fd1f8b
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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