c10r enables multi-agent systems where specialized agents communicate with each other, divide complex tasks into subtasks, coordinate execution, and synthesize results. Supervisor patterns, peer-to-peer delegation, and consensus mechanisms -- all built in.
Choose the pattern that fits your workflow -- or combine them for sophisticated multi-agent orchestration
A supervisor agent decomposes incoming requests, delegates subtasks to specialized workers, monitors their execution, and assembles the final result. Ideal for complex, multi-step operations.
Agents communicate directly with each other, requesting capabilities they lack. A voice agent delegates data lookup to a research agent, which in turn asks an enrichment agent for additional context. No central coordinator needed.
Multiple agents evaluate the same input independently and vote on the outcome. Use this for critical decisions where you want redundancy -- fraud detection, content moderation, or high-stakes classification.
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The supervisor pattern is the most common multi-agent architecture in c10r. A supervisor agent receives a high-level task, breaks it into subtasks, assigns each to a specialized worker agent, monitors execution progress, handles failures with retries or fallbacks, and assembles the final output.
Each worker agent operates in its own context with its own tools and permissions. The supervisor only sees status updates and results -- it does not interfere with how each worker accomplishes its subtask.
Agents in c10r communicate through a typed message protocol. Every message includes sender identity, intent, payload, and expected response schema. This ensures agents understand each other precisely, without ambiguity or misinterpretation.
Messages are logged, traceable, and replayable. You can inspect the full communication graph for any multi-agent execution -- see who asked what, who responded with what, and how the final decision was reached.
For high-stakes decisions, c10r supports consensus-based multi-agent evaluation. Multiple agents independently assess the same input, each using different models, prompts, or evaluation criteria. The system aggregates their outputs and applies a configurable voting rule -- majority wins, unanimous required, or weighted by agent confidence.
This pattern is particularly effective for fraud detection, content moderation, lead scoring, and any classification task where false positives or false negatives carry significant cost.
Build systems where agents specialize, collaborate, and deliver results that no single agent could achieve alone. c10r makes multi-agent orchestration practical.