GAP-03 · Agent architecture
Multiagent Orchestration Board
Current decision
+31% quality for 2.4× cost and 2.3× latency
- Quality change
- 19 model points
- Cost multiple
- 2.4×
- Latency multiple
- 2.3×
Same instrument · 3 industries choosing a scenario replaces its inputs; save or share your current changes first
Prefer to read? The two minute case study · problem → approach → metric → outcome
Problem
The enterprise question is not whether multiagent workflows are technically possible. The question is whether the quality gain is worth the added coordination, runtime, observability, and cost.
Approach
The board shows multiple role agents coordinating through a modeled agent to agent workflow. It tracks how decomposition, role specialization, critique, and synthesis affect the final result and the economics of producing it.
Why this way
This connects architecture design to financial impact, service level expectations, user experience, and operational complexity.
The metric
The head to head scorecard: quality delta vs the single agent baseline and the cost and latency multiples, the ratio, not the demo, is the decision.
The trade-off
More agents raise quality and cost and latency together; the single agent baseline finishes first at lower quality. The lab makes that tension literal.
Outcome
A per task class verdict on whether multiagent is worth it, with the tradeoff quantified, the judgment a delivery leader is accountable for.
Task brief · one persistent request
Ready to inspect
Supervisor
Ready to explain
Researcher
Gather the entrant's public product claims, pricing, and integration model.
Analyst
Compare their approach to ours and find the gaps.
Writer
Assemble a one page brief: the three things leadership must know.
Critic
Red team the brief for unsupported claims.
A2A style coordination · task lifecycle: assigned → working → completed
No messages yet.
Assembled result
Choose Assemble or Show all to inspect the authored final result. It is also available in the complete timeline below.
Multiagent vs single agent
These are complete illustrative outcome values. The explanation does not accrue real cost or measure elapsed model time.
Quality
multi wins
+31%
Cost / run
baseline wins
+139%
Latency
baseline wins
+129%
Agent timeline · why multiagent is slower
The agents run in series, each hands off to the next, so their spans add up. That's the latency you buy for the quality. Per agent durations are an even split of the authored 9.6s (illustrative); the sequence is the point.
Read the complete timeline and result
| Stage | Illustrative duration | Task/output |
|---|---|---|
| Decompose | 0.96 seconds | Prep a competitive brief on a new fintech entrant in card disputes. |
| Researcher | 1.92 seconds | API first disputes, usage based pricing, no on prem, SOC 2 only. |
| Analyst | 1.92 seconds | Their edge: speed to integrate. Ours: regulated scale + governance. Their gap: no case level audit trail. |
| Writer | 1.92 seconds | Drafted 3 point brief on integration speed, governance moat, and audit trail watch. |
| Critic | 1.92 seconds | Flagged one unsupported superlative; softened 'fastest in market' to 'positions on speed'. |
| Assemble | 0.96 seconds | They win on integration speed, API-first, live in days, but SOC 2 only and no on-prem. We win on regulated scale and governance, case-level audit trail is a moat they don't have. Watch: their audit-trail roadmap. If they close that gap, the speed advantage compounds. |
Complete authored result
{
"task": "brief",
"mode": "SIMULATED",
"single": {
"quality": 62,
"costUsd": 0.018,
"latencyS": 4.2
},
"multi": {
"quality": 81,
"costUsd": 0.043,
"latencyS": 9.6
},
"result": [
"They win on integration speed, API-first, live in days, but SOC 2 only and no on-prem.",
"We win on regulated scale and governance, case-level audit trail is a moat they don't have.",
"Watch: their audit-trail roadmap. If they close that gap, the speed advantage compounds."
]
}If you act on this · the call → expected lift → how you'd measure it
The call
Use multiagent orchestration only when the quality lift justifies the cost and latency multiple.
Expected lift · illustrative
Improves output quality for tasks that benefit from decomposition, critique, and role specialization.
How you'd measure it
Quality lift, cost multiple, latency multiple, rework reduction, user acceptance.
When multiagent is worth it
Steering committee takeaway: The decision is not whether the workflow looks sophisticated. The decision is whether the quality gained per dollar and per second is high enough for the task.
How this is built
Orchestration pattern: a supervisor decomposes the goal and delegates to role specialized agents that coordinate over A2A style messages with explicit lifecycle states (assigned → working → completed).
The run is authored and deterministic, a scripted supervisor/worker trace with illustrative cost, latency, and quality figures for this task class, not measured from a live model. A real model variant against claude-sonnet-5 is designed for but not wired today, so the badge stays SIMULATED.
Stack: Next.js (static) + shared design system; client side.
Limitations: the model uses deterministic scoring and authored scenarios. Production orchestration would require live model calls, trace storage, routing policies, evaluation data, and failure handling.