LLM Agent
Definition
An LLM agent combines a language model's reasoning capabilities with the ability to take actions in the world through tools. Unlike a standard chatbot that generates one response per user message, an agent operates in a loop: it receives a goal, plans how to achieve it, selects and calls appropriate tools, observes the results, updates its plan, and iterates—potentially making dozens of tool calls before producing a final answer or completing an action. The agent's behavior is controlled by an orchestration layer (frameworks like LangChain, LlamaIndex Agents, AutoGPT, or Anthropic's Claude API with tool use) that manages the tool-call loop, maintains state, and handles errors. Agentic LLMs are the foundation of autonomous AI assistants.
Why It Matters
LLM agents represent the shift from AI assistants (which answer questions) to AI workers (which complete tasks). A support chatbot answers 'How do I set up webhooks?'; a support agent could actually configure webhooks for the user by calling configuration APIs. A research assistant chatbot summarizes documents; a research agent could search the web, read papers, cross-reference sources, and write a comprehensive report. For 99helpers customers building automation, agents enable handling multi-step customer requests—account upgrades, integration setups, report generation—without requiring a human agent. The key challenge is reliability: agents compound errors across steps, so quality on individual tool calls must be very high.
How It Works
Agent architecture: (1) task receipt—agent receives goal from user or system; (2) planning—LLM reasons about how to achieve the goal (may use chain-of-thought or explicit planning); (3) tool selection—LLM chooses which tool to call and generates arguments; (4) execution—orchestration layer runs the tool; (5) observation—tool result is added to the agent's context; (6) decision—LLM decides whether to call another tool, update the plan, or deliver a final response; (7) repeat steps 3-6 until done. The ReAct framework formalizes this: Thought (reasoning) → Action (tool call) → Observation (result) → Thought... Common agent patterns: React, Plan-and-Execute, LLM Compiler (parallel tool calls).
LLM Agent — ReAct Loop (Thought → Action → Observation)
User query
"What's the weather like in London today?"
I need to check the current weather in London.
Call weather_tool(location="London")
Result: 14°C, partly cloudy.
Now I can answer the user's question.
It is 14°C and partly cloudy in London right now.
Available tools
Loop continues until LLM decides it has enough information to answer
Real-World Example
A 99helpers 'account management' agent handles the request 'I'd like to upgrade to the Pro plan and set up Slack notifications for new conversations.' Without an agent: 3 separate support tickets. With the agent: (1) get_account_info(user) → confirms current plan; (2) check_plan_availability('Pro', user_region) → available; (3) upgrade_subscription(user, 'Pro') → success; (4) get_notification_settings(user) → shows current settings; (5) configure_slack_integration(user, webhook_url) → success; (6) verify_configuration(user) → confirmed. Six tool calls, one conversation, zero human support agents involved. End-to-end completion in 12 seconds.
Common Mistakes
- ✕Giving agents access to consequential actions (billing, deletion, sending emails) without human-in-the-loop confirmation—agents make mistakes; irreversible actions require safeguards.
- ✕Building long-horizon agents without testing failure recovery—agents fail when tools error, return unexpected results, or go into loops; test failure scenarios explicitly.
- ✕Not limiting the number of agent iterations—without a maximum step limit, agents can loop indefinitely, consuming tokens and time without converging.
Related Terms
Tool Use
Tool use is the broader capability of LLMs to interact with external systems—executing code, browsing the web, querying databases, reading files—by calling tools during generation to retrieve information or take actions.
Function Calling
Function calling enables LLMs to request the execution of predefined functions with structured arguments, allowing AI systems to interact with external APIs, databases, and tools rather than just generating text.
Agentic RAG
Agentic RAG extends basic RAG with autonomous planning and multi-step reasoning, where the AI agent decides which sources to query, in what order, and whether additional retrieval steps are needed before generating a final answer.
LLM API
An LLM API is a cloud service interface that provides programmatic access to large language models, allowing developers to send prompts and receive completions without managing model infrastructure.
Reasoning Model
A reasoning model is an LLM that explicitly 'thinks' through problems in an extended internal reasoning process before producing a final answer, trading inference speed for dramatically improved accuracy on complex tasks.
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