Agent Architectures: Control Loops, State & Planning › Summary

Agent architectures give the structural framework that turns Large Language Models from stateless text generators into goal-directed systems able to sustained, adaptive behavior. This chapter examined four pillars of agent design, illustrating how each component contributes to coherent and reliable autonomous operation.

The agent loop establishes the basic control flow: Observation and Reasoning followed by Action, cycling continuously until task completion. Whether implemented as simple single-step loops or complex hierarchical structures, the loop is what turns static inference into dynamic interaction with the world. Each iteration produces an observation that informs the next. This creates a chain of context-aware decisions that can accumulate toward goals too complex for any single model call.

State management addresses the challenge of maintaining context across iterations. Through conversation history, working memory, episodic memory, and tool state, agents accumulate information that informs future decisions. The tension between complete state and limited context windows drives architectural innovations in summarization, hierarchical organization, and external retrieval. Effective state management distinguishes agents that build coherently on prior work from those that repeat the same mistakes across iterations.

Planning strategies determine how agents approach complex tasks. From reactive single-step planning to deliberative hierarchical task networks, the planning component structures agent behavior toward goal achievement. Effective architectures balance plan stability with adaptive replanning, letting agents to pursue coherent long-term strategies while remaining responsive to unexpected findings. The representation of plans as linear sequences, state machines, or programmatic structures shapes the flexibility and reliability of the agent's approach to novel situations.

Termination conditions bound agent execution, preventing infinite loops and resource exhaustion. Whether triggered by goal achievement, resource limits, or safety constraints, termination mechanisms ensure that agents remain practical tools rather than uncontrolled processes. Defining success criteria, whether through explicit external signals or implicit coverage heuristics, requires careful consideration of both user needs and operational constraints.

Together, these components form the blueprint for autonomous language AI systems. While significant challenges remain in reliability and efficiency, with safety requiring equal attention, the architectural patterns established in this chapter are the foundation on which increasingly capable agents are being built. The next chapter extends this foundation by examining how long-term memory systems allow agents to accumulate knowledge and learn from experience across many conversations, not just within a single session.