An AI agent is an LLM-powered system that perceives its environment, plans actions, invokes tools, and pursues goals autonomously across multiple steps — in contrast to single-turn chat or completion systems that respond to one input at a time without persistent state or tool use. The architecture usually has four parts: a **planner** (the LLM reasoning about what to do next), a set of **tools** (functions, APIs, search, code execution) the agent can call, an **orchestration loop** that runs plan → act → observe → repeat until a stopping criterion, and some form of **memory** (short-term within the loop, longer-term across sessions).
The pattern exploded in 2023 with the ReAct paper and AutoGPT demos, matured through 2024 with production-grade frameworks (LangGraph, CrewAI, AutoGen, LlamaIndex agents, OpenAI Assistants, Anthropic's tool-use loop), and standardised further in 2025-26 through the Model Context Protocol (MCP) which provides a common tool-server contract independent of the agent framework. By 2026 the interesting design decisions have shifted from "how do I build an agent" to "how do I build one that is reliable, observable, and cost-bounded in production".
For APAC mid-market enterprises, agents are the right pattern when a task genuinely requires multiple decision steps over interactive state — research tasks, ticket triage with live system lookups, document generation pipelines that assemble from multiple sources, coding assistants that iterate on edits. Agents are overkill when a task can be accomplished by a single prompt + RAG, or by a deterministic workflow with one or two LLM calls at specific steps. The default architecture for a new workload should be **prompt + tools**, not **agent loop**; escalate to agent only when the problem demonstrably needs multi-step autonomy.
The non-obvious operational note: **agent reliability degrades with loop depth**. Every additional step is another opportunity for the planner to pick the wrong tool, misinterpret an observation, or drift from the goal. Production agents impose hard iteration budgets, require an explicit "final_answer" tool, log every step for observability, and often incorporate verification or critic stages that validate progress. A 3-step agent that runs consistently beats a 10-step agent that sometimes succeeds brilliantly and sometimes loops forever.
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