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Multi-agent orchestration coordinates agents, tools, and control flow for a task.
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Delegation can structure work, but extra agents add communication, latency, and failure paths.
Multi-agent systems assign roles and route subtasks among agents. AutoGen is a framework for building applications in which customizable agents converse and can combine language models, tools, and human input. An orchestrator-worker workflow is one possible design, not a guaranteed improvement. Clear termination criteria, provenance, and error checks help prevent loops and unchecked error propagation. The AutoGen framework paper and documentation describe conversable agents that exchange messages and may combine language models, tools, and human input. Its UserProxyAgent can be configured to request human input or execute code; actual behavior depends on configuration. Other frameworks are not described by the cited AutoGen sources.
A lead agent delegates research questions and synthesizes cited findings.
A workflow defines a maximum number of turns and a success condition.
A team logs agent messages so it can inspect disagreements and provenance.
AutoGen demonstrates customizable agents communicating through conversation.
Frameworks and APIs evolve. Measure quality, latency, and cost on the target task; retain human review for high-impact decisions and avoid assuming multiple agents make an answer more reliable.
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Multi-agent orchestration coordinates agents, tools, and control flow for a task. Delegation can structure work, but extra agents add communication, latency, and failure paths.
Orkestrasi membagi pekerjaan kompleks di antara agen-agen yang terfokus (perencana, pembuat kode, kritikus, dll.) dan mengoordinasikan interaksi mereka.
The orchestrator commonly routes work and synthesizes worker outputs.
Multiple agents can add cost and propagate errors without reliable checks.
Aturan penghentian diperlukan agar agen tidak saling bertukar tangan tanpa henti tanpa menemukan jawaban.
The cited AutoGen paper and its official documentation describe a framework for conversable agents that can integrate language models, tools, and human input. This item refers to AutoGen only.
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Pembelajaran Penguatan Multi-Agen
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