The last few years have taught us something important: large language models are astonishingly capable, but they are not magic. Ask one to parse data, clean it, deduplicate, reason about it, summarize it, verify itself… You'll see how fast outputs drift and hallucinations creep in. Debugging becomes a nightmare of staring at an unmanageably long prompt.
The models are not to blame here. It's a failure of design. The lesson is one engineers have known for decades: monoliths don't scale.
Let's talk multi-agent orchestration.
From Monoliths to Brigades
Think of a busy restaurant. There's not one head chef trying to cook every dish, mix every sauce, bake every dessert, check every plate before it leaves the kitchen, and expedite the food right?
That's why restaurants avoid this, by running a brigade de cuisine. Fancy term, I know. A brigade de cuisine is a team of specialists, each with a defined role, all coordinated by the head chef.
Agentic systems benefit from the same structure. Asking one agent to do the work of many is a recipe (ha) for disaster. Instead, break a complex task into smaller roles, give each to a focused agent, and let an orchestrator manage the flow.
- One agent filters inputs.
- Another enriches them with metadata.
- A third summarizes, and yet another verifies.
- Deterministic tools — the code equivalent of kitchen timers and thermometers — handle the parts where ambiguity is unacceptable. (Don't overcook that steak!)
The payoff is predictability. Each agent has a narrow scope, so prompts are shorter, easier to tune, and easier to test. Failures can be caught and recovered from without toppling the whole system. And the workflow itself, the orchestrator, becomes the head chef — the single source of truth for how the kitchen works together.
A Proof of Concept: Research Summaries
To make this idea tangible, I built AI Research Weekly, a proof of concept that ingests arXiv papers and produces weekly digests. Not because the world desperately needs another way to read arXiv, but because it's a really solid way to demonstrate the orchestration pattern. You can explore the implementation here.
The workflow looks like a kitchen service in motion:
- The Prep Cook (Fetcher Tool): brings in fresh ingredients (papers), chopped and validated.
- The Saucier (Screening Agent): discards stale or duplicate items.
- The Entremetier (Enrichment Tool): adds side dishes — concepts, citations, metadata.
- The Grill Cook (Scoring Tool): sears the candidates into ranked order, with reasons attached.
- The Pastry Chef (Summarizer Agent): distills complexity into something sweet and digestible.
- The Expediter (Verifier Agent): checks each plate before it leaves the pass.
- The Head Chef (Compiler Tool): sends it out neatly plated — a Markdown brief and JSON manifest.
The important part isn't the menu. It's how the system shows that specialized agents, working in sequence, produce results that are more reliable, more explainable, and more maintainable than any single massive agent prompt.
Why This Pattern Matters
Simply put, it addresses the exact problems teams face when trying to deploy AI systems in production:
- Governance: The orchestrator decides when each agent runs, like a chef calling tickets.
- Resilience: If one station falters, the rest of the kitchen keeps service moving.
- Auditability: Manifests and logs act like order slips — a paper trail for every decision.
- Extensibility: Need a new dish? Add a new station. The brigade keeps humming.
This is the same reason software engineers abandoned monolithic architectures years ago in favor of services, pipelines, and modular design. Orchestration isn't just about elegance — it's how systems scale.
Looking Ahead
The future of AI engineering isn't set in stone. But right now, it doesn't seem to be in bigger prompts or single "god agents." It's in teams of narrow, reliable agents, stitched together by orchestrators that enforce contracts and provide guardrails.
Proofs of concept like AI Research Weekly aren't the destination; they're signals. They show us that orchestration isn't optional if we want AI systems that are transparent, testable, and trustworthy.