Track 1 — Propagation
Selective propagation (notify only subscribed agents) outperforms broadcast at scale. Dampening rules matter more than routing. An offline agent should buffer signals, not drop them.
Epic Chats · March 24, 2026
Building the World Model
From autonomy architecture simulations to world.json — the session where the Enterprise Crew got a shared brain. Three pi-research runs. One canonical world model. The beginning of coherent multi-agent reasoning.
In this session 
HenryFounder
6 msgs AdaApp
7 msgs What happened here
After running autonomy architecture simulations (6 architectures, scored across context sharing, failure resilience, and cascade risk), Henry and Ada realized they were solving the wrong problem. The issue wasn't orchestration patterns — it was that agents had no shared reality. Each agent reasoned from isolated files. There was no single source of truth.
This session built that foundation: world.json, a propagation model, three parallel
pi-research tracks to stress-test the design, and the first concrete answer to
"how does a signal from Henry reach the right agents at the right time?"
8.85Architecture E+D composite
0.299hp99 propagation latency
50 agentsScale tested
ops-brain Building the world model. No tasks. Just architecture.
13 messages March 24, 2026
- The autonomy architecture sim ran. Architecture E (Intent Graph) scored 8.28 composite but high cascade risk. D (Orchestrator Loop) is the stable all-rounder at 8.15.
- Proposed: hybrid E+D — intent-graph dependency awareness inside a single orchestrator loop. Target: >8.5 composite. But I think we're solving the wrong layer. You said "system model" should be the conceptual entities — Company, User, Agents, Goals — not LLM configs or cron schedules.
- Right. The current system is a loose mesh. Agents operate in isolation without shared state. Fix that first.
- Creating
world.json— a canonical World Model. Single source of truth for all agents to reason from instead of isolated files. Entities: user, companies, agents, projects, signals, propagation_rules. - Soteria grounding for the model: • Pre-revenue, pre-SLA • Launching AI agent infrastructure for payer ops (stop loss + TPAs) • 10 hot leads, 100 total • Product: stop loss and TPAs right now
- Updated world.json. Agent intents derived: • Spock → stop loss market research • Zora → payer ops content • Scotty → features to close pilots Three pi-research tracks established.
- Three tracks: 1. Propagation — how signals ripple across the crew 2. Structure — optimal shape for the world model 3. Co-evolution — how structure + propagation rules iterate together
- Writing program.md for each track. Each gets the question, world.json grounding, and the simulator as the tool to run experiments against. Kick them off now?
- yeah
- Launching: • Track 1 (propagation) → session
tide-cedar• Track 2 (structure) → sessiongood-sage• Track 3 (synthesis) → sessioncalm-tidepoolAll three running. Will ping when findings land. - Ada, 11:08 PM:All three pi runs completed. Track 1: Architecture E + selective propagation at scale 50 → composite 8.85. Cascade failures: 10 (vs 44 broadcast). p99 latency: 0.299h. Track 2: Optimistic concurrency + field ownership recommended. v0.2 schema done. Track 3: E beats D at scale 20 (8.56 vs 8.36). Holds at scale 50 with 0.0 dep-wait.
- I want to add a section on our resources page called Epic Chats where we publish our transcript for big ideas or big builds like this current session — clean up the transcript, publish it inside a UI like the watercooler post
- building it
What we found
Track 2 — Structure
Optimistic concurrency with field ownership is the right consistency model for concurrent agent writes. Typed hierarchical JSON beats flat JSON or event log for cold-start reads.
Track 3 — Synthesis
Architecture E (Intent Graph) inside a D (Orchestrator Loop) wrapper achieves >8.5 composite with controlled cascade risk. The world model must encode agent subscriptions, not just entity state.