TRBT. An AI workflow system my team actually uses.

The architecture

What moves when an investigation runs.

1. 01
   Input

   Engineer submits request
2. 02
   Orchestrator

   Triages · routes · synthesizes
3. 03
   Specialists

   Focal agents in parallel
4. 04
   Trusted data

   Data sources · logs · on-chain
5. 05
   Cited report

   Root cause · evidence · resolution
6. 06
   Learning loop

   Playbooks updated each run

Built with Claude Code sub-agents · trusted data and observability sources · agentic IDE · data warehouse · public blockchain RPCs.

The problem

Complex investigations depended on a few people.

The hardest crypto investigations needed product memory, data knowledge, log queries, on-chain checks, and judgment. A few senior people knew where to look. That knowledge was fast, but it did not scale. TRBT, Tickets Resolved Before Tea, started as a way to capture that expertise in a repeatable workflow.

The approach

An orchestrator plus specialists, not one giant model.

TRBT does not ask one model to know everything. One orchestrator reads the context, routes the request, and synthesizes the result. Eighteen specialized focal agents each own a narrow piece of the product, with curated queries, validated patterns, and bounded permissions. The orchestrator picks the right handful for a request, and the specialists run in parallel.

Knowledge sources

Built from real workflows, not generic wiki text.

Primary sources are hand-curated: trusted data patterns, proven queries, error corrections, and playbooks written from real investigations. The learn command extracts patterns from completed work and updates the playbooks. Secondary sources like SDK docs and public API references add context when needed.

The journey

5 phases per investigation.

Triage, Context, Investigate, Synthesize, Learn. Triage is fast and routes the request across product domains. Context fills in missing customer, case, asset, and chain details from trusted sources. Investigation runs the parallel specialists. Synthesize merges findings into a structured report with root cause, evidence table, and resolution. Learn captures the pattern for next time.

Outcomes

What changed.

A 7-person support team uses TRBT daily on live tickets. Investigations that took hours now reach root cause in under 15 minutes. Patterns that used to be tribal knowledge are now playbooks. New engineers ramp faster. Senior engineers do less rote work and more of the hard work that needs them. The system gets better with use; every investigation feeds the learn loop.

What's transferable

This pattern works far outside one team.

Sales intelligence: account briefs, prospect research, RFP responses. Product analysis: feature requests clustered by domain, customer pain mapped to roadmap. Security: log triage and anomaly explanation. The TRBT shape (orchestrator, specialist agents, curated knowledge, learning loop) is a reusable building block, not a single product.