Multi-agent Systems
Multi-agent Systems coordinate.
When one agent can't hold the full context — or when different sub-tasks need different domain expertise — the right answer is multiple agents under a supervisor, not one mega-agent. We architect the topology, the contracts between agents, and the eval suite that catches when they disagree.
- Supervisor / specialist / critic / planner topologies — picked by job.
- Inter-agent contract: who can speak, what they're allowed to say, who has veto.
- Conflict resolution and human-in-the-loop escalation paths.
- Eval suite that scores the system end-to-end, not just per agent.
“Multi-agent isn't an answer. It's the question — `do we need this complexity?` — and the architecture if the answer is yes.”
§ 02 / Authority
From the roomsI've been in — and the feeds that came out of them.
Live shorts. LinkedIn originals. Unedited.
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PMI Gujarat · Keynote · Dec 2024
I had the incredible opportunity to be part of the PMI Gujarat Chapter — NEXTGEN Project Management, elevating our world through emerging technologies.
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Karnavati · AI Conclave 2025
Unlocking the Power of AI: it's all about the right prompt. At Karnavati University, I had the privilege of sharing a key insight…
Rajkot · Keynote · 2025
What happens when AI meets India's social and rural future? You get a room full of students, academics, and leaders in Rajkot asking the questions that actually matter.
PM Forum · Off-script · 2025
I walked into a room of 45+ project managers. No slides. No deck. No script.
- PMI Gujarat
- Karnavati University
- Atmiya AI Summit
- HP WeRise · Ahmedabad
- Project Managers' Forum
- NIFT Gandhinagar
- MeitY · Digital India
- UIT Karnavati
- Ahmedabad founder circle
- PMI Gujarat
- Karnavati University
- Atmiya AI Summit
- HP WeRise · Ahmedabad
- Project Managers' Forum
- NIFT Gandhinagar
- MeitY · Digital India
- UIT Karnavati
- Ahmedabad founder circle
What this means in practice
We start with the contract: which sub-tasks are real, which roles are needed, what each role refuses to do. Then we build a supervisor that owns the conversation and per-agent specialists with narrow remits.
- 01
Topology diagram + per-agent contract (system prompt, tool access, escalation rules).
- 02
Supervisor implementation — coordinates the conversation, holds shared memory, calls specialists.
- 03
Specialist agents — each with its own eval suite and golden tasks.
- 04
End-to-end eval — measures the system, not just each part. Catches "all agents passed individually but the whole thing failed" cases.
Engagement shapes
2 ways to engage on Multi-agent Systems. Pick the closest fit; we calibrate scope on the diagnostic call.
01
1 wk
Multi-agent design review
If you're already running one agent and considering more, we audit whether multi-agent is actually the right call before you commit. Often it isn't.
02
8–12 wk
Multi-agent build sprint
Full design + build + evals. Hand-off includes the topology doc + the failure-mode register so future engineers don't redesign from scratch.
For
Who this fits
- Teams running long-horizon workflows (legal-review, claims-processing, deep-research) where one agent is provably insufficient.
- Engineering orgs that already have 1+ single-agent system in production and now hitting context-window or domain-expertise limits.
- Operations leads automating cross-functional processes where the workflow itself spans roles.
Not for
Honest gates
- Teams who picked multi-agent because it sounded sophisticated — start with one agent + a checklist of why one isn't enough.
- Pure tech demos. Multi-agent without a real bottleneck just multiplies cost and failure modes.
FAQ
About this service.
When is multi-agent NOT the right answer?
When one agent + a checklist would do. When the tasks aren't actually different (just verbose). When the team can't articulate why one agent isn't enough. About 60% of "we want multi-agent" enquiries get redirected to single-agent + better prompting.Cost compared to a single agent?
Materially higher — every agent burn-through is multiplicative. Plan for 3–5× the per-request cost of an equivalent single agent. The trade-off: you can solve workflows that a single agent can't, and the supervisor topology often beats one mega-prompt on quality.How do agents share context?
Three patterns: (a) shared blackboard (Redis / Postgres) the supervisor reads / writes; (b) message-passing with explicit hand-offs; (c) hybrid — each specialist owns its state, supervisor orchestrates. We pick the pattern from the workflow shape on the design call, not religiously.Conflict resolution — what if agents disagree?
Built into the contract. Either the supervisor adjudicates (with rules), or a critic-agent scores both outputs, or it escalates to human. We design the failure path before the success path; agents that disagree silently are a much worse outcome than agents that escalate cleanly.Eval methodology for multi-agent?
End-to-end primary, per-agent secondary. The system passes when the workflow output is correct, not when each agent passes individually. Catches the "every agent thought it was doing its job, but the system failed" class of bug — which is the most common multi-agent bug.When does it hand off to human?
Always defined upfront. Every supervisor has explicit escalation triggers — confidence below threshold, conflicting outputs, missing tool, time-out. Human-in-the-loop is a feature of the architecture, not a fallback we add later.
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Voices from the rooms
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PMI Gujarat · Keynote · Dec 2024 Karnavati · AI Conclave 2025 Rajkot · Keynote · 2025 PM Forum · Off-script · 2025
Next step
Tell me what you’re trying to ship.
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