Multi-Agent Orchestration Patterns We Ship in Production

Agentic AI

Single-agent demos work in three hours. Multi-agent production needs three weeks of orchestration design. The four patterns we ship and which one fits when.

By Arjun Raghavan, Security & Systems Lead, BIPI · April 8, 2026 · 8 min read

Building a single agent that calls a few tools is a weekend project. Building a system of agents that hand work to each other reliably, observe their outputs, and recover from failures is a quarter of work. Most teams reach for multi-agent setups expecting the first kind of effort and underestimate the second by 10x.

We have shipped enough multi-agent systems now to recognise that almost all of them are one of four patterns. Each pattern fits a different shape of problem. Picking the right one before you write code is the most leveraged decision in the project.

Pattern 1: Sequential pipeline

Agent A produces output. Agent B receives that output, transforms it, hands to Agent C. Like a function pipeline. Linear, deterministic, easy to observe.

Use when: the work has a fixed shape and each stage requires different specialised reasoning (extraction, then enrichment, then formatting). Document processing pipelines. Resume parsers. Compliance review chains.

Avoid when: the work needs branching or iteration. Sequential pipelines fall apart the moment the work needs to revisit an earlier stage with new context.

Pattern 2: Supervisor + workers

A supervisor agent receives the goal, decomposes it into subtasks, dispatches each to a specialist worker agent, collects results, synthesises the final output. The supervisor knows the overall plan; the workers know one tool well.

Use when: the problem can be decomposed but the decomposition depends on the input. Customer support triage that routes to billing-bot, technical-bot, or escalation-bot based on intent. Research tasks where the planner picks search-agent vs database-agent vs code-agent dynamically.

Avoid when: the supervisor's planning is brittle (the LLM hallucinates a worker that does not exist) or when the workers need to coordinate with each other beyond the supervisor's view.

Pattern 3: Critic loop

Agent A produces a candidate output. Agent B reviews it against a rubric, returns either approval or specific critique. If critique, Agent A revises. Loop until Agent B approves or a max-iterations cap fires.

Use when: quality matters more than latency. Code generation with a separate code-review agent. Research summaries with a fact-check agent. The critic-as-different-model trick (use Claude as the writer, GPT-4 as the critic, or vice versa) catches more issues than self-review.

Avoid when: the critic agent agrees with the writer too easily. This pattern requires that the critic be calibrated, not sycophantic. Calibration is non-trivial.

Pattern 4: Marketplace bidding

Multiple worker agents each produce a candidate response. A judge agent picks the best, optionally with explanation. Discarded candidates are not used.

Use when: you have multiple credible approaches to a problem and you do not know which works on this particular input. Code generation across three different prompting strategies. Translation across three different model families. The cost is N times the inference of a single agent; the quality lift is real for hard problems.

Avoid when: the cost is unjustified. For most workflows, a single agent with a critic is cheaper and almost as good.

Observability matters more than architecture

All four patterns need full tracing of every agent invocation, every tool call, every inter-agent message. Without traces you cannot debug, cannot tune, cannot evaluate. The 'three weeks of orchestration design' we mentioned at the top is mostly observability work, not prompt engineering. Get the traces right and the patterns come out cleanly.

Closing

Multi-agent systems are not a category of difficulty above single-agent systems; they are a different category of difficulty. Pick the pattern that fits the work, instrument the traces, and resist the urge to add more agents than the problem actually requires. Most projects we audit could be one well-designed agent with two tools instead of four agents with twenty.

Read more field notes, explore our services, or get in touch at info@bipi.in. Privacy Policy · Terms.