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Cognitive agent

As explained in AI Agents in Action, Second Edition

A cognitive agent is an agent that decomposes novel tasks, reasons about dependencies, composes tools, and updates its understanding as new information arrives.

A cognitive agent builds a task representation, decomposes novel problems, reasons about dependencies, composes tools, and updates its plan when evidence changes. Cognition here means observable, engineerable capabilities rather than machine consciousness. section 10.1.3

A standard ReAct agent can execute a reasoning-and-action pattern, while a cognitive agent extends that pattern with novel task decomposition, dependency-aware sequencing, tool composition, and model updating. section 10.1.3 section 10.4.2

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

Use the capability table to connect each cognitive behavior with its failure mode and supporting primitive. section 10.1.3

Recognize cognition through four behaviors:

  • Decompose unfamiliar problems into subtasks instead of treating complex queries as flat lookups. section 10.1.3
  • Sequence dependent steps correctly and identify work that can proceed in parallel. section 10.1.3
  • Chain available tools in configurations that were not explicitly demonstrated. section 10.1.3
  • Revise a hypothesis or plan when new evidence contradicts the initial approach. section 10.1.3

Attention returns the agent to planning

In a cognitive cycle, perception classifies a query as complex, planning proposes HYPOTHESIS_TEST, execution returns low-confidence results, and evaluation signals REPLAN. Attention routes the cycle back to planning rather than advancing blindly. section 10.5

Memory improves later work

After one troubleshooting session records the problem, strategy, and outcome in a knowledge graph, proactive retrieval can surface that experience during a related second query and inform its plan. section 10.5 section 10.4.2

How AI Agents in Action, Second Edition covers it

How deep it goes
Defines four capabilities and maps them to behaviors and primitives, then exercises attention, memory, and calibration without discussing model internals.
Examples
Uses attention-routing traces, troubleshooting sessions, knowledge-graph retrieval, and confidence-versus-correctness comparisons.
What it assumes
Assumes attention and MCP modules for implementation exercises, including memory and filesystem server connections.
What you can do after
Build and evaluate a cognitive loop that replans after weak evidence, retrieves experience, and measures confidence calibration.
How it connects
Connects task decomposition, dependency reasoning, tool use, model updating, ReAct, planning, replanning, attention, memory, and confidence calibration.

Related concepts

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.