Agent Swarms¶
Problem Statement¶
Run many identical agents on the same task, aggregate results: index documents, process events, analyze data. How do we partition work, parallelize, and aggregate results?
Solution Approach¶
Work Partitioning:
100 documents to index
8 swarm agents
Partition: documents[0:12], [12:25], [25:37], [37:50], [50:62], [62:75], [75:87], [87:100]
Each agent indexes independently
Results: 8 index shards
Aggregate: merge shards into final index
Result Aggregation:
Counting: count(agent1_results) + count(agent2_results) + ...
Averaging: mean(agent1_scores, agent2_scores, ...)
Voting: majority(agent1_decision, agent2_decision, agent3_decision)
Merging: combine_indexes(shard1, shard2, ...)
Scalability:
Bottleneck analysis:
- Partition time: typically < 1% (simple split)
- Processing time: (total_work / num_agents) - speedup is ~linear
- Aggregation time: depends on strategy (merge slower than sum)
10,000 documents, 100 agents:
- Sequential: 100 sec
- Parallelized: 1 sec (partition + processing) + aggregation time
When to Use¶
- Use for embarrassingly parallel work (document indexing, batch analysis)
- Use when agents are identical and interchangeable
- Skip if agents need coordination (use multi-agent instead)