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AI agent

As explained in Inside AI

An autonomous system is a system that performs functions and makes operational decisions with limited direct human control.

An AI agent is a system that pursues a goal with limited direct human control. A smart agent perceives changes in its environment, responds to them, and can communicate with other agents. Its autonomy concerns how it operates toward a defined goal; it does not imply human-like understanding or self-awareness. section 4.0 section 9.4

Coordinated fraud and security monitoring

Imagine separate agents watching network traffic, customer accounts, employee access, and digital communications. An unusual account event can prompt other agents to check related activity, then raise an alert or block suspicious activity. The value lies in sharing specialized observations quickly, rather than expecting one component to recognize every pattern alone. section 4.0

The smart-agent approach combines several capabilities:

  • Reactivity and goals: agents respond to changes while pursuing an intended outcome. section 4.0
  • Communication: attributes can participate in exchanges, and mailboxes receive messages from other agents or external sources. section 4.2
  • Adaptation: agents can update their view of an entity’s behavior over time; in fraud prevention, customer and merchant profiles are proposed as continuously updated signals. section 4.1.1

This differs from a conventional search that explores permitted moves from an initial state toward a goal state: an agent-based system distributes work among entities that react and communicate. Agents can also be combined with rules, case-based reasoning, fuzzy logic, and other AI techniques. The proprietary MINDsuite platform and its AGORA language illustrate one such approach, but a platform name is not part of the definition of an agent. section 4.1 section 4.1.2 section 11.7

Autonomy calls for careful evaluation, especially when errors have serious consequences. An incident involving autonomous target acquisition and another involving automated tracking illustrate how unexpected interactions or misleading identifiers can contribute to dangerous outcomes. Neither autonomous operation nor adaptive behavior guarantees reliable judgment. section 9.4

How Inside AI covers it

How deep it goes
Defines agent capabilities and contrasts them with state-space search, but leaves full technical treatment outside its overview.
Examples
Illustrates agents with collaborating financial-security specialists, continuously updated fraud profiles, and a proprietary platform.
What you can do after
Evaluate/understand where cooperating, adaptive agents might help and why autonomy still requires attention to operational risk.
How it connects
Connects agents to distributed problem-solving, state-space search, adaptive learning, and hybrid AI systems.

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This explainer was generated by Manning's knowledge graph from the text of Inside AI. The section links point to the parts of the book it draws on.