What is AI agent?
Also called: Agentic AI · LLM agent · Autonomous agentDefinition
An AI agent is a software system in which a large language model decides what steps to take towards a goal and carries them out using tools, such as searching documents, calling APIs, running code or updating business systems. The agent works in a loop: it plans, acts, observes the result and decides the next step until the task is done or it needs human input. Agents can automate multi-step work, but they need clear permissions, guardrails, logging and evaluation because their errors can compound across steps.
The core components are the model, a set of tools with clear descriptions, instructions that define the goal and limits, and memory or state that carries information between steps. Frameworks help orchestrate the loop, but many reliable agents are simple: a defined workflow where the model makes a small number of decisions at known points.
For organisations, agents extend generative AI from answering questions to doing work: triaging tickets, reconciling records, researching accounts or preparing first drafts that pull data from several systems. The value is highest in repetitive, well-defined processes with clear success criteria and a human approving consequential actions.
Common mistakes are giving agents broad permissions too early, building fully autonomous flows where a fixed workflow would be more reliable, and skipping evaluation of whole tasks. Start with narrow scope, read-only tools, human approval for actions that change data or reach customers, and full logs of every step.
Key points
- An LLM that plans and acts using tools in a loop.
- Suited to multi-step, well-defined processes.
- Needs scoped permissions and human approval for consequential actions.
- Errors can compound across steps, so evaluate whole tasks.
- Simple, structured workflows are often more reliable than full autonomy.
An example at work
A Pune SaaS company deploys an agent that reads incoming support tickets, searches the knowledge base, drafts a reply and proposes a category, while a support executive approves each reply before it is sent.
Where this is used at Bodhih
Related terms
Large language model
A large language model (LLM) is an AI model trained on vast amounts of text to understand and generate language by predicting the next token.
Retrieval-augmented generation
Retrieval-augmented generation (RAG) is a technique where an AI system retrieves relevant documents first and gives them to an LLM to ground its answer.
LLM evaluation
LLM evaluation is the systematic testing of a language model or LLM application against defined criteria to measure quality, accuracy and safety.
Responsible AI
Responsible AI is the practice of designing, deploying and using AI so that it is fair, safe, transparent, accountable and respects privacy.
Generative AI
Generative AI is a type of artificial intelligence that creates new content, such as text, images, audio, video or code, from patterns learnt from data.
More about AI agent
What is the difference between an AI agent and a chatbot?
A chatbot responds to messages, usually one turn at a time. An AI agent pursues a goal over several steps and takes actions with tools, such as querying systems, running code or updating records. Many modern assistants blend both: they chat with the user but can also act as an agent when given tools.
Are AI agents safe to use in business?
They can be, if designed carefully. Limit each agent to the tools and data it needs, require human approval before actions that change records or contact customers, log every step, test on realistic scenarios before launch, and monitor in production. Treat agent permissions with the same care as an employee’s system access.