An AI agent is a system built around a Large Language Model that can autonomously plan multi-step actions, reason about intermediate results, use external tools (APIs, databases, code execution, web search), and adapt its approach based on outcomes — going far beyond simple question-answering to accomplish complex real-world goals. Where a basic LLM generates text in response to a prompt, an agent observes its environment, makes decisions, takes actions, observes the results, and iterates until the task is complete. AI agents represent the shift from AI as a tool (human gives instruction, AI responds once) to AI as a worker (human assigns a goal, AI figures out and executes the steps autonomously).
Why it matters
AI agents represent the next paradigm in how organizations use AI — moving from individual prompts to delegated tasks. Instead of "Summarize this document," an agent handles "Research competitor pricing, compile a comparison spreadsheet, identify where we're overpriced by more than 20%, and draft price adjustment recommendations for the product team." This shift multiplies AI impact from a per-interaction productivity boost to full workflow automation. However, agents also introduce new challenges: reliability (each tool call can fail, and errors compound across steps), safety (an agent with database write access can cause real damage), cost predictability (complex tasks may require dozens of LLM calls), and alignment (the agent's interpretation of a vague goal may differ from what the human intended). Understanding agent architectures and their failure modes is essential for safe, effective deployment.
How it works
An AI agent typically follows a sense-plan-act loop. It receives a task, decomposes it into sub-steps (planning), executes the first step using available tools (acting), observes the result (sensing), and decides the next action based on what it learned. The LLM serves as the reasoning engine — interpreting task descriptions, selecting which tools to call with which parameters, analyzing tool outputs, and deciding when the task is complete. Tool access is defined by the agent's toolset: web search, code execution, file system access, API calls, database queries, email sending, or any external capability exposed through a function interface. Agent frameworks (LangChain, CrewAI, AutoGen, custom implementations) provide the orchestration layer that manages the sense-plan-act loop, handles tool execution, maintains conversation state, and enforces guardrails like maximum iterations, human approval gates, and output validation.
Example
A procurement team uses an AI agent to handle vendor evaluation. The human assigns: "Evaluate the top 5 cloud storage vendors for our compliance requirements." The agent autonomously: 1) Searches the web for current cloud storage vendor options and pricing. 2) Queries the company's compliance database for required certifications (SOC 2, GDPR, ISO 27001). 3) Visits each vendor's website to extract certification status. 4) Reads the team's budget constraints from a shared spreadsheet. 5) Creates a comparison matrix in a Google Sheet. 6) Drafts a recommendation email highlighting the top 2 vendors with justification. 7) Sends the draft for human review before sending. The entire workflow — which would take a human analyst 4-6 hours — completes in 15 minutes with the agent making 23 tool calls across 6 different systems. The human reviews and approves, making one small edit to the recommendation before it is sent.