What is an LLM agent?

What are the key components of an LLM agent?

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Every LLM-based agent can be thought of as having four working parts:

1. The "brain."
   The large language model itself. It reads prompts, reasons about them, and produces text responses. You give it a prompt, and it uses what it learned from huge text datasets to decide what to say and which actions (or tools) to trigger. Think of it as a navigator that first needs clear directions before it sets off.
   
2. Planning.
   Before diving in, the agent breaks a big request into smaller steps (plan formulation) and revisits that plan as new information arrives (plan reflection). Methods such as chain-of-thought and tree-of-thought help it reason through multi-step problems, while feedback loops like ReAct or Reflexion let it adjust on the fly.
   
3. Memory.
   A store of past interactions or facts that the agent can recall so it doesn't start from scratch each time and can keep long conversations coherent. To keep conversations coherent, the agent keeps two notebooks. A short-term pad tracks the current exchange and is cleared once the task ends. A long-term diary stores useful facts and patterns from past interactions so the agent can recall preferences and improve over time.
   
4. Tool use.
   When language alone can't finish the job, the agent calls external tools (calculators, APIs, databases, code runners, and more). Frameworks such as MRKL, Toolformer, TALM, HuggingGPT, or API-Bank teach the model when and how to pick the right tool, retrieve results, and fold them back into its answer.
   

What are the types of LLM agents?

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LLM agents aren't one-size-fits-all. They're built in different "flavors," each tuned for a particular job and industry setting. Here are the most common types and typical places you'll meet them:

What is the difference between LLM and AI agent?

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Put simply, the LLM is the brain; the AI agent is the whole body that thinks, remembers, plans, and acts in your environment. Think of it like that:

• LLM = language engine.

It reads text in, predicts text out. Great for writing, summarising, translating and spotting patterns in documents, but it doesn't decide, remember, or act on the world by itself.

• AI agent = doer built around an LLM (plus other parts).

It watches for inputs, plans next moves, calls tools or APIs, even controls robots or workflows. The LLM supplies the "speech and reasoning," while extra code, memory and connectors let the agent make decisions and carry them out.

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What are the benefits and limitations of using LLM agents?

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In short, an LLM agent can ­supercharge automation and decision-making, but it needs clear boundaries, well-prepared data, good prompts, and ongoing human oversight to avoid costly surprises.

But here are benefits and limitations explained:

Main benefits

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• Strong problem-solver – It can break a big request into a step-by-step plan, write code, run benchmarks, and hand back clear summaries.
• Self-checking – It tests its own code, or searches the web to double-check facts, catching many errors before you see them.
• Learn on the fly – When it spots a mistake it can revise its work, or even ask a second agent to critique the answer and improve it.
• Tool-friendly – From databases to APIs and spreadsheets, it can pull in outside tools to get the job done faster.
• Team player – Multiple agents can divide a complex project (e.g., research, QA, coding) and combine their results for better overall quality.

Key limitations

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• System access risk – If it touches live databases or services without strict guardrails, you can end up with tangled "spaghetti" code, heavy loads on production data, or accidental data leaks.
• Short memory – The model can only hold a limited context window, so it may forget earlier instructions or details in a long conversation.
• Weak long-term planning – It adapts poorly to surprises; complex projects still need a human in the loop to steer and course-correct.
• Inconsistent output – Because everything is expressed in natural language, formatting slips and instruction drift can creep in.
• Prompt sensitivity – Slight wording changes cause big swings in quality, so good prompt engineering (or fine-tuning) is essential.
• Role inflexibility – Making the agent act as a niche expert or show true empathy often demands extra training data you may not have.
• Data hurdles – If the underlying data isn't clean, timely, and properly secured, the agent's answers—and your compliance posture—suffer.
• Compute cost – Processing large inputs or running many agents in parallel can get expensive and slow unless you tune models and workloads carefully.