AI Agent

An autonomous AI system that can perceive its environment, make decisions, use tools, and take actions to accomplish goals with minimal human intervention.

In Depth

An AI agent is an autonomous system built around a language model that can perceive its environment through inputs and observations, reason about the current state and objectives, make decisions about what actions to take, execute those actions using tools and APIs, and iterate based on results until a goal is accomplished. Unlike simple chatbots that respond to individual queries, agents maintain context across multiple steps and can handle complex, multi-stage tasks with minimal human intervention.

The core architecture of an AI agent typically includes a reasoning engine (usually an LLM) that interprets goals and plans actions, a memory system that maintains context across interactions, a tool registry that provides access to external capabilities (APIs, databases, file systems, web browsers), and an execution loop that orchestrates the observe-reason-act cycle. Frameworks like LangChain, LlamaIndex, AutoGPT, and CrewAI provide abstractions for building agent systems.

Agent capabilities are rapidly expanding from simple tool-calling patterns to sophisticated multi-agent architectures. Single agents handle linear workflows with tool access. Multi-agent systems assign different roles (researcher, planner, coder, reviewer) to specialized agents that collaborate on complex tasks. Hierarchical agent systems use manager agents to coordinate teams of worker agents. Agentic workflows combine predetermined process structures with flexible agent decision-making at each step.

Enterprise agent applications include research automation (gathering, synthesizing, and reporting on information from multiple sources), code generation and software engineering assistance, customer service escalation handling, data analysis and report generation, and process automation that bridges multiple business systems. Key challenges in production agent deployment include reliability (agents can fail or loop), cost management (multi-step reasoning consumes many tokens), safety (agents with tool access can take consequential actions), and observability (understanding agent decision-making for debugging and auditing).

Related Terms

Agentic Workflow

An AI-driven process where language models autonomously plan, execute, and iterate through multi-step tasks using tools, memory, and decision-making.

Function Calling

The ability of language models to generate structured output that invokes external functions or APIs, enabling interaction with external systems and data.

Chain-of-Thought (CoT)

A prompting technique that improves AI reasoning by instructing the model to break down complex problems into explicit intermediate steps.

Large Language Model (LLM)

A neural network with billions of parameters trained on massive text corpora that can understand, generate, and reason about natural language.

Prompt Engineering

The systematic practice of designing and optimizing input prompts to elicit accurate, relevant, and useful outputs from large language models.

Related Services

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Refactoring AWS, Azure, GCP, and Oracle workloads into production-grade AI stacks. Multi-cloud RAG pipelines, observability, guardrails, and MLOps that slot into existing engineering rhythms.

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In-a-box deployments for Enterprise Research copilots, Enterprise RAG pipelines, and Video Search & Summarisation agents with interactive Q&A. Blueprints tuned for your data, infra, and compliance profile.

Custom Model Training & Distillation

Training domain models on curated corpora, applying NeMo and LoRA distillation, and wiring evaluation harnesses so accuracy stays high while latency and spend drop.

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