What Are AI Agents? Fundamentals, Architecture, and Workflows by IBM

LinkAnna GutowskaResource

An IBM guide explains AI agents as autonomous systems that leverage LLMs to plan workflows, break down tasks, and execute tool calls. Unlike traditional language models limited to static training data, agentic systems dynamically retrieve data via APIs, external tools, or other agents, adapting through iterative reflection and feedback loops.

Key points

  1. Core mechanics: Agents use LLMs to perform autonomous task decomposition, dynamic planning, and tool calling without constant human intervention.
  2. Iterative reasoning and reflection: Continuous self-correction, memory retention, and human-in-the-loop (HITL) or multi-agent feedback refine decision-making.
  3. Enterprise case study: Dynamiq implemented a multi-agent legal research assistant using IBM watsonx Orchestrate and an IBM Granite classifier, reducing contract review time from 90 to 45 minutes.
  4. Ecosystem overview: The comprehensive guide outlines architectural patterns (ReAct, ReWOO), protocols like Model Context Protocol (MCP), and frameworks including crewAI, BeeAI, and AutoGen.

Why it matters

For software engineers and tech leaders, agentic AI represents the shift from passive text generation to autonomous systems capable of orchestrating complex workflows, interacting with APIs, and integrating deeply into business processes.

Analysis

IBM categorises the emerging agent landscape into core primitives like task decomposition, reasoning, and standard protocols (e.g., MCP, ACP). The real-world examples highlight an architectural trend: efficient multi-agent systems use tiered routing—placing low-cost classifiers before specialised research agents—to balance operational costs with performance.

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Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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