How AI agents use reasoning loops, memory, and tools to complete complex multi-step tasks autonomously — going far beyond a single prompt-response exchange.
A single LLM in a loop with access to tools. On each turn it writes a reasoning trace, picks one tool, observes the result, and repeats. Simple to implement and surprisingly capable for well-defined tasks.
An orchestrator agent decomposes the task and delegates sub-tasks to specialized agents — a coder, a researcher, a critic — each with their own tools and context. Enables parallelism and deep specialization.
A planning phase first generates the full list of steps without executing them. An executor then works through the plan step by step. Separating planning from doing reduces mid-task drift on long-horizon tasks.
The LLM answers in a single shot from its training data. It cannot look anything up, run code to verify, or break a complex task into steps. Multi-step problems require the user to manually chain prompts, copy outputs between steps, and babysit each stage. Errors compound silently.
The LLM plans, acts, and self-corrects in a loop. It searches for current information, writes and runs code to test its answers, reads files, calls APIs, and tries alternative approaches when one fails — all without human intervention at each step. Complex tasks become tractable.
The agent receives the user's goal as a system prompt, alongside descriptions of every available tool. These tool schemas (name, parameters, description) tell the LLM what it can do without hard-coding any logic. The agent also loads relevant long-term memories — past task outcomes, user preferences — so it starts informed, not blank. This context setup is the "working memory" the agent will reason over.
The LLM generates a thought: a scratchpad sentence explaining what it knows, what's missing, and what to try next. Then it emits a structured tool_use block — tool name and arguments — which the host intercepts and routes. The agent never runs code itself; it outputs structured intent and the host executes it. This separation keeps the agent auditable: every decision is written out in the transcript before it happens.
The tool result is appended to the context as an observation message. The LLM now has new information: a search result, code output, a file's contents, an API response. It reads this, updates its reasoning, and decides whether the goal is met or another step is needed. This Observe → Think → Act loop is what makes agents adaptive — they respond to what actually happened, not what they assumed would happen.
After each observation the LLM checks a stopping condition: Is the goal achieved? Have all sub-tasks completed? Has a maximum step limit been hit? When it determines the task is done, it emits a final answer message — synthesizing everything in the context into a coherent response. Good agents also report what they did (tool calls, sources) so the user can verify the work, not just trust the conclusion.