Multi-Agent Systems › Worked Example: Research and Report Pipeline

To make these patterns concrete, let's trace through a multi-agent research pipeline for the query: "What are the key factors affecting electric vehicle adoption in Europe?"

Task decomposition by orchestrator:

1. Research agent A: Find recent statistics on EV adoption rates by country
2. Research agent B: Identify infrastructure (charging station) coverage data
3. Research agent C: Review policy incentives across EU member states
4. Analysis agent D: Synthesize findings from A, B, C into key factors
5. Writer agent E: Write a final structured report from D's analysis

Agents A, B, and C run in parallel since they do not depend on each other. The analysis agent D waits for all three to complete before beginning. The writer agent E waits for D.

Communication flow:

The orchestrator writes task assignments into a shared workspace with unique task IDs. Each research agent reads its assignment, executes web searches and data retrieval using its tools, and writes structured findings back to the workspace tagged with the task ID and agent identity.

When all three research tasks show status "completed," the orchestrator spawns the analysis agent with the combined findings from A, B, and C in its context. The analysis agent produces a structured summary of key factors and writes it to the workspace. The orchestrator then spawns the writer agent with this summary.

Conflict handling:

Research agents A and C both find data on government subsidies but report different figures. The analysis agent D, instructed to cite sources and flag discrepancies, notes the inconsistency and includes both figures with their sources rather than arbitrarily choosing one. The writer agent E presents this as a range with appropriate caveats. This is a practical illustration of why conflict resolution mechanisms matter: without instructions to flag discrepancies, the analysis agent might silently pick one figure, producing a misleadingly precise result.

Output quality vs. single agent:

The parallel search dramatically reduces time: three simultaneous web searches take roughly the same wall-clock time as one, rather than three times as long. The specialized research agents each go deeper into their domain than a single general-purpose agent would. The dedicated analysis and writing stages produce cleaner separation between raw data gathering and synthesis.

A fair comparison would also run a single high-quality agent with a budget equivalent to five sequential agent calls, using chain-of-thought and self-critique loops to compensate for the lack of parallelism and specialization. In practice, the multi-agent approach tends to win on breadth (more ground covered) and the single-agent approach sometimes wins on coherence (no stitching artifacts from combining outputs from multiple agents with different writing styles).