The Planning Problem in Autonomous Systems

Most teams start with single-shot LLM calls and quickly hit a wall when building agents that need to operate autonomously. The shift from stateless prompting to stateful agents requires explicit planning mechanisms. ReAct patterns combine reasoning traces with action execution, allowing the agent to verbalize its thought process before each tool call. However, this breaks down in complex scenarios where a single reasoning step isn't enough. Plan-and-Execute architectures separate high-level goal decomposition from execution, creating a structured task breakdown before invoking tools. The key trade-off is latency versus robustness. Dynamic replanning based on execution feedback adds overhead but prevents agents from blindly following outdated plans when the environment changes.

Memory Architecture Design

Production agents require three distinct memory types working in concert. Episodic memory stores conversation history and recent interactions, typically implemented as sliding window buffers or vector stores with recency weighting. Semantic memory captures domain facts and learned knowledge, often backed by embeddings for retrieval. Procedural memory encodes workflows and skills the agent has mastered through fine-tuning or few-shot examples.

Memory Type Comparison

Tool Integration and Function Calling

Agents gain agency through tool use. Modern LLM APIs support native function calling where the model outputs structured JSON matching function signatures. The implementation challenge is handling partial failures and retry logic. When an agent calls an API that returns an error, it needs the context to understand what went wrong and reformulate the request. This requires passing error messages back into the agent's context window along with the original intent. Tool chaining becomes critical when one tool's output feeds another's input. LangGraph's state machines provide explicit control flow for managing these dependencies, avoiding callback hell that plagues naive implementations.

Multi-Agent Coordination Patterns

Specialization emerges when complexity demands it. Rather than building one superintelligent agent, production systems often deploy multiple focused agents with clear domain boundaries. A router agent triages requests to specialist agents handling specific capabilities. Coordination protocols define how agents communicate, whether through shared memory, message passing, or centralized orchestration. Conflict resolution matters when agents disagree. Voting mechanisms, confidence scoring, or hierarchical authority structures determine which agent's output takes precedence. The engineering challenge is avoiding circular dependencies and ensuring termination conditions exist in agent-to-agent dialogues.