Learn · AI Engineering
What is agentic AI?
Agentic AI is software that uses a language model to plan and take multi-step actions toward a goal, calling tools (APIs, databases, other systems) along the way. The minimal pattern: a model + a set of tools + a control loop. The model decides which tool to call next based on what it has seen so far.
Updated · 2026-05-04 · 8 min read
Agent vs. chatbot
A chatbot turns user input into a response and stops. An agent turns user input into a plan, executes that plan by calling tools, observes the results, and revises until the goal is met or it asks for help.
Take the question "Where is my order?" A chatbot reads from a knowledge base and replies with whatever the FAQ says about delivery times. An agent queries the orders API, checks the shipping system, identifies that the package is delayed, drafts a refund offer, posts it to the ticket queue, and emails the customer. Same input — fundamentally different system.
Agentic AI vs. generative AI
Generative AI is a capability: producing text, images, code, audio. Agentic AI is an architectural pattern that uses generative AI to drive autonomous, multi-step action with tools. All agentic AI uses generative AI under the hood; not all generative AI is agentic.
A summarization endpoint is generative but not agentic. GitHub's Copilot suggesting code is generative but not agentic. A customer-support agent that reads tickets, looks up orders, and posts replies is both. The agentic pattern is what unlocks measurable business outcomes — generative alone is interesting; agentic is what delivers ROI.
The minimal agent pattern
Strip every popular framework — LangGraph, AutoGPT, CrewAI, Pydantic AI — back to the essentials, and you're left with three components running in a loop:
- 01A model. Usually a frontier-tier LLM (Claude, GPT, Gemini) for tool-using reasoning. Open-weight models (Llama, Mistral, Qwen) are catching up but trail on tool-call reliability.
- 02A set of tools. Typed function definitions the model can invoke. Each tool has a name, description, JSON-schema input, and a runtime that returns a result. APIs, databases, side-effecting actions.
- 03A control loop. Call the model with the conversation history; if the model requests a tool, run it and append the result; loop until the model produces a final answer or hits a termination condition (max steps, cost cap, error).
Production agents add evaluation, guardrails, observability, and human-in-the-loop checkpoints around that core loop. Read the full build guide →
Three production examples
Customer-support deflection
Tools: orders API, knowledge base, ticket-system writer, refund-issuer. The agent reads the ticket, gathers evidence, drafts a reply, and either posts directly (low stakes) or queues for human review (refunds, escalations). Typical AISD outcome: 25–40% auto-resolution rate.
Document processing pipeline
Tools: OCR, structured-data extractor, validator, ERP writer, exception queue. Inbound documents (contracts, claims, invoices) are parsed, extracted into a typed schema, validated against business rules, and routed. Exceptions go to a human queue. Typical AISD outcome: 30–50% reduction in human review time.
Sales-outbound research agent
Tools: company-search, news-search, LinkedIn lookup, CRM writer. For each lead, the agent gathers a recent news angle, identifies the right buyer persona, drafts a personalized opener, and writes it back to the CRM with a confidence score. Typical AISD outcome: 2–4× research throughput per SDR.
Orchestration patterns
Three patterns dominate production agentic AI in 2026:
- Single-loop ReAct. One agent, one loop, all tools available from step one. Best for simple workflows — one tool per step, no parallelism.
- Plan-and-execute. The agent first writes a plan (a list of steps), then executes each step. Better for complex tasks where premature tool calls would waste cost.
- Multi-agent graph. Multiple specialized agents (researcher, writer, reviewer) handing off via a defined graph. LangGraph and similar frameworks shine here. Required when sub-tasks are fundamentally different.
Most production agents AISD ships are single-loop ReAct or plan-and-execute. Multi-agent is rarer than vendor marketing implies — most "multi-agent" deployments are actually one agent with carefully designed tools.
Where agents fail
Four predictable failure modes you'll hit at production scale:
- Tool errors. An API times out, returns malformed data, or rate-limits. The agent doesn't recover gracefully. Fix: typed tool schemas with retry + circuit-breaker logic.
- Prompt injection. User-controlled text (an email body, a scraped page, a document) reaches the agent and overrides its instructions. Fix: input sanitization, privilege separation, output validation, adversarial test suite in CI.
- Cost spirals. An agent that loops without termination conditions burns inference budget. Fix: per-session cost caps, max-step limits, monitoring on token spend per execution.
- Distribution shift. Input patterns change after launch and the agent's prompts no longer match reality. Fix: weekly eval re-runs, input distribution monitoring, drift alerts.
Getting started
If you're scoping your first production AI agent:
- 01Pick one use case with high volume, structured input/output, and a clear success metric.
- 02Map the tools the agent will need before writing prompts. Each tool is an integration; that's where most build time goes.
- 03Build the eval harness on day one. A golden test set of 50–500 inputs, scored automatically and reviewed by humans on a sample. Run on every PR.
- 04Pick orchestration based on workload, not novelty. Most projects don't need multi-agent.
Or skip the framework selection and run a discovery sprint — we'll architect it for you in two weeks.