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Agents demystified

How modern AI agents perceive, reason, and act

Think of an agent as a controllable teammate: it reads what you allow, reasons through the request, and takes approved actions. This guide explains how it works, the design trade-offs, and how to run it safely.

Learn the fundamentalsSee latest updates
01

Perceive first

Establish facts from documents, APIs, or the screen before planning or acting.

02

Reason simply, then deeply

Begin with a minimal plan; add deliberate loops for ambiguous or long tasks.

03

Act safely

Each tool call carries intent, scoped permissions, logging, and rollback.

04

Operate continuously

Maintain observability and human oversight; refresh memories on schedule.

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Core principles

Three loops power every successful agent

Diagram of core agent capabilities
Source: IBM AI Agents overview
Perceive

Acquire context from search, APIs, files, or the screen; materialize a working memory.

  • Hydrate memory via retrieval/APIs/sensors.

  • Normalize state for grounded and auditable reasoning.

Reason

Translate goals into steps; combine heuristics with deliberate loops as needed.

  • Mix fast heuristics with tool-using reflections.

  • Use guardrails to prevent overreach and error accumulation.

Act

Invoke tools/services with explicit intent and scoped permissions; trace all steps.

  • Transaction logs and rollbacks bound autonomy.

  • Evaluate outcomes to improve reliability and trust.

Agent lifecycle

A resilient pipeline keeps perception, planning, and action honest

Diagram showing agent flow from query to answer
Source: IBM React Agent overview
01

Ingest

Collect domain knowledge, live signals, and constraints; hydrate episodic and semantic memories with clear TTLs.

Knowledge base, connectors, data contracts

02

Plan

Draft a task graph, assign tools, simulate risky steps, and establish approvals/limits.

Task graph, guardrail policy, evaluation hooks

03

Act

Run steps with tracing; stream outputs; each call includes intent, scope, and rollback.

Tool adapters, workflow runners, audit log

04

Learn

Score outcomes, refresh memories, correct drifts, and escalate when confidence is low.

Offline evaluation, memory compaction, feedback loops

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Want to dive deeper into the complete lifecycle?

Deep dive into ReactAgent's complete workflow from initialization to execution, including core mechanisms, tool implementations, and architectural highlights.

View full documentation→

Ecosystem map

Key building blocks for shipping trustworthy agents

Diagram of agent ecosystem layers
Source: IBM AI Agents overview

Foundation & Reasoning

Model choice determines depth, latency, and cost; reserve stronger models for hard steps.

  • Frontier & compact models; structured multi-step reasoning

  • Select via task-level evaluations, not hype

Orchestration

Coordinate memory, tools, and state; prefer stateful graphs and resumable runs.

  • Stateful graphs; open tool protocols (e.g., MCP)

  • Policies/permissions and traces as first-class concerns

Safety & Governance

Define boundaries and approvals; implement programmable guardrails.

  • Guardrails (e.g., Colang), rate limits, content filters

  • Human checkpoints for high-risk actions

Deployment & Operations

Operate like software: tracing, evaluations, canaries, and rollback.

  • Observability platforms; dataset-based evaluations

  • Cost/latency dashboards and continuous improvement

Multi-agent patterns

Coordinate intent across multiple agents and humans

Shared planners, specialized workers, and human checkpoints let you compose reliable agent teams.

Diagram of multi-agent collaboration patterns
Source: IBM Agentic Architecture

Central planner

One planner coordinates multiple specialist agents with shared memory and guardrails.

  • Keeps tooling centralized, easier to audit.

  • Escalate to humans when planner confidence drops.

Agent marketplace

A router selects from a pool of agents based on skill tags and historical performance.

  • Requires consistent scoring and rate limits per agent.

  • Cache frequent tasks to reduce selection latency.

Hybrid loops

Long-running workflows pair agents with named human roles for approvals or final delivery.

  • Surface context packs for humans to act quickly.

  • Log human decisions back into agent memory.

Tools & references

Roll out responsibly with these playbooks

Protocols & Core APIs

Platforms & Orchestration

Safety & Observability

Benchmarks & Evaluation Suites

Latest updates

Live agent intelligence feed

Curated by an automated monitor that scrapes vendor blogs, research feeds, and policy trackers.

Last updated: Jan 17
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