What is an AI agent?

An AI agent is software that perceives its environment, makes decisions, and takes actions to achieve goals — with some degree of autonomy. Unlike a chatbot that responds to prompts, an agent operates in a loop: observe the current state, decide what to do, act, observe the result, and repeat until the goal is met. Stanford's 2025 AI Index Report defines AI agents as "systems that can autonomously plan, use tools, and execute multi-step tasks to achieve user-defined objectives." The term has become overloaded in 2025-2026 — everything from a ChatGPT wrapper to an autonomous coding system gets called an "agent." The differences matter.

Unlike generic AI automation posts, this guide shows real CodeWords workflows — not just theory.

Related reading: custom AI agents, create your own AI agent, build your own AI agent, AI workflow automation, what is hyperautomation, CodeWords integrations, CodeWords templates.

Why AI agents matter now

Three converging developments made AI agents practical in 2025-2026:

LLMs that can use tools. Models from OpenAI, Anthropic, and Google can now call functions, execute code, search the web, and interact with APIs based on natural language instructions. The model reasons about which tool to use, calls it, interprets the result, and decides the next action.

Cheaper inference. Model costs dropped by roughly 10x between 2023 and 2025 (a16z AI report). Running an AI agent that makes 20 model calls per task became economically viable for production workloads, not just demos.

Better orchestration infrastructure. Platforms that manage the agent loop — tool execution, state management, error recovery — matured enough for production use. CodeWords provides this infrastructure: serverless execution, multi-model access, state persistence, and 500+ tool integrations.

How AI agents work

The core agent loop:

1. Perceive — The agent receives input: a user request, a webhook trigger, new data, or the result of a previous action
2. Plan — The LLM reasons about what steps are needed to achieve the goal
3. Act — The agent executes the chosen action: calling an API, running code, searching the web, or making another LLM call
4. Observe — The agent receives the action's result
5. Iterate — Steps 2-4 repeat until the goal is achieved or a stopping condition is met

What makes this different from a simple automation workflow: the agent decides what to do at each step based on the current context. A traditional workflow follows a fixed path. An agent adapts.

Types of AI agents

Single-task agents handle one specific capability: summarize this document, classify this email, extract data from this invoice. These are the most reliable because their scope is narrow and their success criteria are clear. Most production AI automation falls here.

Multi-step reasoning agents chain multiple actions toward a goal: research a topic by searching the web, reading relevant pages, synthesizing findings, and producing a report. CodeWords deep research workflows implement this pattern — multiple data sources, AI synthesis, structured output.

Autonomous agents operate with minimal human oversight across broad task domains: manage my inbox, handle customer support, monitor my competitors. These are the most ambitious and the least reliable. Autonomy and reliability are currently in tension.

Multi-agent systems use multiple specialized agents that collaborate: one agent researches, another writes, a third reviews. Each agent is focused and reliable; the orchestration layer coordinates them.

Real-world examples

Example 1: Intelligent lead qualification (CodeWords). When a new lead submits a form, an AI agent: scrapes their company website (Firecrawl), searches for recent news and funding (SearchAPI.io), enriches with public data, evaluates against your ICP using an LLM, generates a qualification score and summary, and routes to the right team member via Slack. Each step adapts based on what the agent finds — a funded startup gets deeper analysis than a sole proprietor.

Example 2: Support ticket triage. An agent reads incoming support tickets, classifies by urgency and topic, checks the customer's account status via API, drafts a response if the issue matches known patterns, and escalates to human support if confidence is low. This runs on CodeWords with native LLM access and integration connectors.

Example 3: Research assistant. Given a question, the agent searches multiple sources, reads and evaluates each result, identifies conflicting information, synthesizes findings with citations, and delivers a structured report. This is the deep research pattern running on CodeWords.

The reliability question

The most important thing to understand about AI agents: they're probabilistic, not deterministic. The same input can produce different outputs. An agent that works 95% of the time fails 1 in 20 attempts. For high-volume automation, that failure rate matters.

Production-ready agent implementations address this with: - Structured output validation (Pydantic schemas in CodeWords enforce output format) - Confidence scoring (route low-confidence results to human review) - Fallback logic (try a different model or approach when the first attempt fails) - Guardrails (limit the agent's available actions to prevent unintended consequences)

Anthropic's 2025 agent safety research found that agents with structured output constraints and explicit tool boundaries completed tasks 40% more reliably than unconstrained agents.

Connection to automation

AI agents are the intelligence layer of modern automation. Traditional automation handles the "if this, then that" logic. AI agents handle the "understand this, decide that" logic. The combination — deterministic workflow orchestration with AI agent reasoning at decision points — is where CodeWords operates.

FAQs

Is ChatGPT an AI agent? Base ChatGPT is a conversational AI, not an agent. ChatGPT with plugins, code execution, and web browsing moves closer to agent behavior — it can plan, use tools, and iterate. But it operates within a single conversation, not as a persistent automated process.

Can I build AI agents without coding? You can describe agent workflows conversationally to CodeWords' Cody assistant, which generates the underlying Python code. You don't need to code from scratch, but understanding the agent's logic helps you debug when it behaves unexpectedly.

Are AI agents ready for production? Single-task and multi-step reasoning agents are production-ready with proper guardrails. Fully autonomous agents across broad domains are still experimental for most use cases. Start narrow, validate reliability, then expand scope.

Build production AI agents at codewords.agemo.ai.