Defensible decisions
at the speed of AI.
Common-law logic for how organizations decide: precedent, adversarial review, and a citable record at the moment the call is made.
Upgrade production Kubernetes 1.28 → 1.35
Proposed by @agent-platformer · cites 3 prior upgrades, 7 affected services, 2 teams.
3 CVEs patched. Matches precedent in PRD-1902. Rollout window aligns with low-traffic.
Removes deprecated PodSecurityPolicy. 2 internal charts still depend on it.
Approve with staged rollout. Migrate charts off PodSecurityPolicy in a blocking sub-task. Owner: @platform-core.
A decision, generated in front of you.
Precedent lookup, adversarial debate, and an LLM-as-a-judge convergence — rendered as a citable Architectural Decision Record.
- initInitRegistry session opened
- queryQueryContext submitted
- step 1Memory GraphPrecedents retrieved
- step 2Adversarial DebateArchitect ↔ Skeptic
- step 3LLM-as-a-JudgeConvergence & scoring
- outputADR Emitted089_compute_migration.md
AI collapsed execution cost. Not decision cost.
A platform engineer goes from ~5 a week to ~20 a day — and has to defend each one later.
Regulators want lineage and provenance for every autonomous call. Most teams can't produce it.
Without a defensible record, autonomy stalls. AI gets clipped back to ‘assistant’ mode.
Decisions move like cases through a court.
An institutional memory built like common-law precedent.
Precedent graph
Every decision — who made it, why, and what it was based on — recorded in a queryable graph your agents can reason over.
Impact radius
Before a change ships, see which teams and systems it touches — and who needs to weigh in.
Defensible audit trail
Lineage and provenance for every decision. ‘Why did we do this?’ already has an answer.
Adversarial review
Two agents argue both sides. An LLM judge rules — with confidence and citations.
Plugs into your stack
Github, Slack, Linear, Jira, Datadog, Snowflake — the places your decisions already happen.