Reasoning & Planning
Chain-of-thought, tree search, task decomposition, and planning strategies.
Chain-of-Thought in Agents
Chain-of-thought reasoning within agent loops provides structured scratchpad space where agents reason through complex decisions before acting, reducing errors in multi-step tasks by making the reasoning process explicit and auditable.
Error Detection and Recovery
Error detection and recovery is the agent’s ability to recognize when actions fail or produce incorrect results, classify the type of failure, and apply appropriate recovery strategies ranging from simple retries to full replanning.
Inner Monologue
Inner monologue is the agent’s private reasoning process – hidden thoughts that guide decision-making without being exposed to the user, enabling more honest deliberation, safety checks, and complex multi-step reasoning behind the scenes.
Metacognition
Metacognition is the agent’s ability to reason about its own reasoning – knowing what it knows, what it does not know, when to ask for help, and how to route tasks based on honest self-assessment of its capabilities and limitations.
Plan-and-Execute
Plan-and-Execute separates strategic planning (deciding what steps to take) from tactical execution (carrying out each step), enabling agents to handle complex multi-step tasks with structured oversight and adaptive replanning.
ReAct Pattern
ReAct interleaves chain-of-thought reasoning with concrete actions, enabling agents to think about what to do, do it, observe the result, and adjust their reasoning accordingly.
Reflection and Self-Critique
Reflection enables agents to evaluate their own outputs and actions after the fact, learn from mistakes through verbal self-critique, and improve performance on subsequent attempts without weight updates.
Task Decomposition
Task decomposition breaks complex, ambiguous goals into atomic, executable subtasks, transforming vague instructions like “research topic X” into concrete sequences of retrievable, verifiable actions.
Tree Search and Branching
Tree search explores multiple solution paths simultaneously, allowing agents to consider alternative actions like a chess player evaluating candidate moves, backtracking from dead ends, and selecting the most promising path forward.
World Models
A world model is the agent’s internal representation of its environment’s state, enabling it to predict consequences of actions, maintain awareness of what has changed, and simulate outcomes before committing to irreversible operations.