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Cognition

As explained in AI Agents in Action, Second Edition

Cognition is an agent’s ability to model a task, decompose problems, reason about dependencies, compose tools, and update its understanding from new information.

Cognition is the quality of an agent’s internal model of a task: how it represents what is being asked, decides how to proceed, and updates its understanding as evidence arrives. It describes observable engineering capabilities, not machine consciousness. Reasoning patterns help solve well-defined problems; cognition helps identify what an ambiguous problem requires, while metacognition monitors whether the agent has understood it. section 10.1.3 section 10.1.1

Four capabilities make up this task model:

  • Task decomposition breaks a novel problem into subtasks without relying on a supplied template. section 10.1.3
  • Dependency reasoning identifies which subtasks rely on others and which can run in parallel. section 10.1.3
  • Compositional tool use combines tools in new configurations to fit the problem. section 10.1.3
  • Model updating revises the agent’s understanding or plan when new evidence contradicts its initial hypothesis. section 10.1.3

From separate lookups to a composed task

An agent may be able to perform two lookups separately yet fail to join or compare their results. Cognition means recognizing that the request needs multiple steps and planning a tool sequence to complete them. section 10.1.1 section 10.1.3

A cognitive agent architecture organizes these abilities around a shared workspace, with perception, planning, execution, evaluation, and attention modules. A memory module can add persistent experience, retrieving past strategies before planning and recording outcomes afterward. section 10.6

Table 10.1 Agent cognitive capabilities

Capability

Present

Absent

Supporting primitive

Task decomposition

Agent invents novel subtask sequences

Agent treats complex queries as flat lookups

CoT, planning

Dependency reasoning

Agent sequences steps while respecting dependencies

Agent executes steps in arbitrary order

ToT, sequential thinking

Compositional tool use

Agent chains tools in novel configurations

Agent maps queries to single tool calls

ReAct

Model updating

Agent revises plan when evidence contradicts it

Agent follows initial plan regardless

Reflexion, replanning

Compare each capability with its absent counterpart and the reasoning primitive associated with it. section 10.1.3

How AI Agents in Action, Second Edition covers it

How deep it goes
Defines four observable capabilities and maps them to reasoning primitives, while explicitly stopping short of claims about consciousness.
Examples
Illustrates tool composition with separate lookups that must be joined or compared to answer a request.
What it assumes
Builds on agentic loops and reasoning patterns including CoT, ReAct, ToT, and Reflexion.
What you can do after
Evaluate agent behavior by checking whether it decomposes novel tasks, respects dependencies, composes tools, and updates its plan.
How it connects
Connects cognition to metacognition, planning, CoT, ToT, ReAct, Reflexion, and agent architectures based on shared workspaces.

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This explainer was generated by Manning's knowledge graph from the text of AI Agents in Action, Second Edition. The section links point to the parts of the book it draws on.