Running multiple agent tasks simultaneously and synthesising the results.
Parallelism is the pattern that makes agent systems fast enough to be useful on complex tasks. Rather than running sub-tasks sequentially, a parallel architecture dispatches independent tasks simultaneously and aggregates results when all (or enough) have completed.
The parallelism pattern has two components: a decomposition layer that breaks a task into independent sub-tasks, and a synthesis layer that combines the results. The key design question is which sub-tasks are genuinely independent — able to run in parallel without needing each other's outputs — and which have dependencies that require sequencing. In practice, most enterprise workflows have a mix: some steps can be parallelised, others must be sequential. A hybrid approach — parallel where possible, sequential where necessary — produces the best balance of speed and correctness. The synthesis layer requires careful design: it must handle the case where some parallel tasks fail, take longer than expected, or produce conflicting outputs.
A due diligence team uses a parallel agent architecture to assess acquisition targets. When a new target is identified, five agents are dispatched simultaneously: one analyses the financial statements, one scans press coverage, one reviews regulatory filings, one checks patent portfolios, and one analyses leadership team backgrounds. A synthesis agent waits for all five outputs, then produces a consolidated assessment. What previously took five analysts two days completes in 40 minutes. The synthesis agent flags contradictions between the financial analysis and press coverage for human follow-up.
Sequential processing of complex tasks produces unacceptable latency for real business workflows. A due diligence process that takes two days when done by humans should not take 90 minutes when done by AI — but without parallelism, it might. Parallelism is the architectural pattern that closes that gap. It also enables a quality pattern: running the same task in parallel with different approaches and comparing outputs to detect hallucinations.
How this pattern fails in practice — and what to watch for.
Two parallel agents both update the same shared data store simultaneously, causing one update to overwrite the other. The final state reflects only one agent's work, but the system reports both as successful.
Three of five parallel tasks complete successfully; two fail. The synthesis layer doesn't have a defined policy for this. Should it proceed with partial results? Wait for retries? Escalate? Without an explicit policy, the behaviour is undefined and potentially dangerous.
A design that runs 20 parallel tasks per user query seems fine in testing with 10 concurrent users, but fails economically and technically when deployed to 10,000 concurrent users. Infrastructure and inference costs were modelled sequentially.
Seven things to verify before deploying this pattern in production.
AIDA examines parallelism under D4 Observability — specifically how parallel executions are traced and correlated when debugging. CAIG covers the governance of parallel agent decisions: who is accountable when five agents each contribute to an output? CAIAUD auditors assess whether partial failure scenarios have defined policies and whether those policies are actually implemented in the deployed system.
The AIDA certification covers all 21 agentic design patterns with a focus on deployment safety, governance, and the PSF. Free to attempt.