Distributes embarrassingly parallel work across multiple AI agents with clear briefs and crisp handoff protocols, then aggregates their results through a single integrator. This technique maximizes throughput when tasks are independent and the coordination overhead is low, making it ideal for research chunks, file batches, or parallel data processing.
Use cases
- Processing a batch of 20 files that each require the same type of analysis or transformation
- Researching multiple independent topics simultaneously before synthesizing findings into a single report
- Building multiple independent modules of a larger system in parallel where integration is deferred to a final step
- Running integration tests against multiple API versions or environment configurations simultaneously
- Generating content variants for A/B testing where each variant is independent and they are combined only at evaluation time
Key features
- Verify that all tasks are truly independent—no shared state, no sequential dependencies, no order-of-execution requirements
- Give each agent an explicit brief: the specific scope of their task, the exact output format required, and any constraints they must respect
- Dispatch all agents simultaneously and wait for all results rather than chaining them sequentially
- Merge outputs in a single integrator step, resolving conflicts or duplications based on the documented priority rules
- Validate the aggregated result against the original brief to ensure no information was lost or distorted in the merge
When to Use This Skill
- When you have multiple independent tasks that would each take significant time if done sequentially
- When the total work can be divided into clear, non-overlapping slices with a well-defined aggregation step
- When you need to complete a data processing or research task within a tight deadline by leveraging parallelism
Expected Output
A consolidated result from all parallel agents, with conflicts identified and resolved, ready for integration or delivery.
Frequently Asked Questions
- What happens when one of the parallel agents fails or produces low-quality output?
- Build retry logic into the dispatch step with a maximum retry count. If retries also fail, log the failure and proceed with the remaining agents. Document the failure in the aggregated output so the integrator can handle missing data.
- How do I prevent parallel agents from duplicating work or conflicting on shared resources?
- Enforce a strict independence property before dispatching. If shared state is unavoidable, use a coordination mechanism (a lock file, a shared database row, or a dedicated aggregator agent) rather than assuming agents will coordinate themselves.
- Is it worth the overhead of parallel dispatch for tasks that only take a few minutes each?
- Only if the total sequential time exceeds your deadline or if the task benefits from faster iteration through overlapping work. For small tasks, the coordination overhead often negates the parallelism benefit.
Related
Related
3 Indexed items
Multi-agent orchestration
Coordinates multiple AI agents on shared tasks with explicit handoff protocols, shared state management, and conflict resolution so parallel work stays coherent. Multi-agent orchestration is more structured than simple parallel dispatch because agents take on distinct roles with explicit dependencies rather than running identical briefs on independent data.
Multi-agent handoff design
Designs clean handoff protocols between specialized agents so work passes between planner, coder, reviewer, and executor agents without losing context, creating circular dependencies, or introducing race conditions. Handoff design treats agent-to-agent communication as an API contract with versioning, error handling, and explicit acknowledgment requirements.
Plugin scaffolding
Bootstraps a complete plugin project structure with manifest files, entry points, configuration schemas, and baseline tests so new Codex or editor extensions follow a consistent, reviewable template from day one. This eliminates the setup tax for creating new plugins and ensures every plugin in a codebase shares the same conventions for configuration, logging, and error handling.