Managing agent execution, maintaining context across steps, and coordinating complex multi-step tasks.
Agent orchestration manages the loop of reasoning and acting, handles tool execution, and maintains state across steps. State management tracks workflow progress, short-term context (conversation), and working memory (intermediate results). Frameworks like LangGraph provide explicit state machines; custom solutions offer more control.
Orchestration is the system that runs the agent loop:
Core responsibilities:
Prompt management:
LLM interaction:
Tool execution:
State management:
Control flow:
Agents need different types of state:
Conversation state:
Workflow state:
Working memory:
Long-term memory:
State persistence options:
Model agent workflows as explicit state machines:
Benefits:
State machine components:
Example workflow states:
LangGraph approach: LangGraph makes state machines explicit:
Some agent tasks take minutes, hours, or days:
Challenges:
Patterns:
Async execution:
Checkpointing:
Time-based triggers:
Implementation:
Managing what goes into the LLM context:
The problem:
Strategies:
Summarization:
Relevance filtering:
Structured state:
Tiered memory:
Monitoring: Track token usage per step. Alert when approaching limits.
Mapping business processes to agent workflows with decision points, human-in-the-loop, and error handling.
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