A framework-agnostic guide to the recurring patterns for building LLM agents — what an agent is (the model directs the flow; agents vs workflows), the core reason-act-observe loop (ReAct), tool use, planning and decomposition, memory (short-term vs long-term), reflection and self-correction (Reflexion), multi-agent patterns, and building reliable agents (and when not to use agents at all).
"Agent" has become one of the most overused and least precise words in AI — applied to everything from a chatbot with a system prompt to a fully autonomous system that writes and ships code. Cutting through the hype requires a clear definition: an agent is a system where an LLM decides its own actions in a loop, using tools, until a goal is met. That one distinction — the model choosing what to do next, rather than following a fixed script — is what separates a genuine agent from a workflow, and it's where both the power and the difficulty come from.
'Agent' has become one of the most overused words in AI. Cutting through the hype requires a clear definition: an agent is a system where an LLM decides its own actions in a loop, using tools, until a goal is met. That one distinction — the model choosing what to do next, rather than following a fixed script — is where both the power and the difficulty come from.
Strip away the frameworks, the tooling, and the jargon, and every LLM agent reduces to one simple loop: think about what to do, do it, look at what happened, repeat. This reason-act-observe cycle — crystallized by the ReAct pattern — is the beating heart of every agent, and understanding it deeply is understanding agents themselves. Once you see the loop clearly, agent frameworks stop being mysterious: they're all just implementations of this same fundamental cycle.
Strip away the frameworks and every LLM agent reduces to one simple loop: think about what to do, do it, look at what happened, repeat. This reason-act-observe cycle — crystallized by the ReAct pattern — is the beating heart of every agent, and understanding it deeply is understanding agents themselves.
An LLM on its own can only do one thing: generate text. It can't search the web, run code, query a database, check the current time, or send a message — it can only produce words. Tools are what break that confinement, turning a model that can only talk into an agent that can act. Tool use is arguably the single most important capability that makes agents possible, and understanding how it works — and how to design tools well — is central to building effective agents.
An LLM on its own can only do one thing: generate text. Tools are what break that confinement, turning a model that can only talk into an agent that can act. Tool use is arguably the single most important capability that makes agents possible.
Ask an agent to "research this market and write a report" and it faces the same problem a person would: the task is too big to do in one leap. The answer, for agents as for people, is to break it down — decompose the goal into steps, and work through them. Planning is how agents handle complexity that the basic reason-act loop alone would fumble, and the patterns for doing it — from planning upfront to decomposing on the fly — are among the most important in agent design.
Ask an agent to 'research this market and write a report' and it faces the same problem a person would: the task is too big to do in one leap. The answer, for agents as for people, is to break it down. Planning is how agents handle complexity that the basic reason-act loop alone would fumble.
An LLM is, fundamentally, stateless — it remembers nothing between calls except what you put in its context window. For an agent that takes many steps or works across many sessions, that's a serious problem: without memory, every step starts from scratch, and nothing is ever learned. Memory is how agents overcome statelessness — holding the context of the current task, and carrying knowledge across tasks and time. Understanding the kinds of agent memory, and their limits, is essential to building agents that can handle real, extended work.
An LLM is fundamentally stateless — it remembers nothing between calls except what you put in its context. For an agent that takes many steps or works across sessions, that's a serious problem. Memory is how agents overcome statelessness — holding the current task's context, and carrying knowledge across tasks and time.
The first output is rarely the best output — a truth as old as writing, and one that applies to agents too. An agent that acts once and moves on repeats its mistakes; an agent that looks back at what it did, judges whether it worked, and tries again can dramatically improve. Reflection — the agent evaluating and correcting its own work — is what turns a one-shot attempt into an iterative process that gets better, and it's one of the most powerful patterns for making agents reliable on hard tasks.
The first output is rarely the best output. An agent that acts once and moves on repeats its mistakes; an agent that looks back at what it did, judges whether it worked, and tries again can dramatically improve. Reflection turns a one-shot attempt into an iterative process that gets better.
The instinct, once single agents work, is to build teams of them — a researcher agent, a writer agent, a critic agent, all collaborating like a little organization. It's an appealing vision, and sometimes exactly right. But multi-agent systems are also where a lot of complexity and cost hides, and the honest guidance is more restrained than the hype: use multiple agents when the problem genuinely calls for it, and prefer a single well-designed agent when it doesn't. Understanding the multi-agent patterns — and their real tradeoffs — is what lets you make that call well.
The instinct, once single agents work, is to build teams of them — a researcher, a writer, a critic, collaborating like an organization. Sometimes that's right. But multi-agent systems are also where a lot of complexity and cost hides, and the honest guidance is restrained: use multiple agents when the problem genuinely calls for it, and prefer a single well-designed agent when it doesn't.
The demo is easy; the production agent is hard. An agent that works impressively in a demo can fail unpredictably in the real world — looping, hallucinating, taking wrong actions, or racking up huge costs — because the same flexibility that makes agents powerful makes them unreliable. Building agents that actually work in production is a discipline of managing that unreliability: adding guardrails, evaluating rigorously, and, most importantly, knowing when not to use an agent at all. This closing post is about that discipline.
The demo is easy; the production agent is hard. The same flexibility that makes agents powerful makes them unreliable — looping, hallucinating, taking wrong actions, racking up costs. Building agents that actually work is a discipline of managing that unreliability, and knowing when NOT to use an agent at all.
This series is part of a larger body of work by Pratik Dhanave, an Agentic AI Architect writing about production AI systems, distributed systems, and cloud-native engineering. Explore all course series, browse every post, or find topics via the tag index.