What is agentic AI?

Agentic AI refers to AI systems that can autonomously plan, execute, and iterate on multi-step tasks with minimal human intervention. Unlike a standard LLM call where you send a prompt and get a response, an agentic AI system receives a goal ("research our top 10 competitors and compile a pricing comparison"), breaks it into sub-tasks, executes each one, evaluates the results, adjusts its approach when something fails, and delivers the final output.

The word "agentic" distinguishes these systems from passive AI (answering questions) and copilot AI (suggesting next steps for a human to execute). An agent acts. It calls APIs, reads documents, writes files, and makes decisions about what to do next based on intermediate results. Unlike generic AI automation posts, this guide shows real CodeWords workflows — not just theory.

Andrew Ng popularized the term in his 2024 writings, identifying four agentic design patterns: reflection, tool use, planning, and multi-agent collaboration. Gartner named agentic AI as a top strategic technology trend for 2025-2026, predicting that 15% of day-to-day work decisions will be made autonomously by AI agents by 2028.

Related: what is intelligent automation, AI workflow automation, build your own AI agent, custom AI agents, make AI agents, CodeWords integrations, CodeWords templates.

How agentic AI differs from standard LLM usage

Standard LLM call: You send "Summarize this article" and get a summary back. One input, one output. The model doesn't take any actions — it just generates text.

Agentic AI: You send "Find the five most recent articles about our competitor, summarize each one, identify common themes, and post a digest to our team's Slack channel." The agent: 1. Plans the task: identify search queries, determine how many articles to retrieve, decide on summary format 2. Executes: calls a search API, retrieves article URLs, scrapes content via Firecrawl, passes each to an LLM for summarization 3. Synthesizes: identifies themes across summaries, generates the digest 4. Acts: posts to Slack via API 5. Verifies: checks that the message posted successfully, retries if not

Each step involves decisions. The agent might find that one article is behind a paywall and decide to skip it, replacing it with the next result. It might find only four relevant articles and adjust its approach. This adaptability is what makes the behavior "agentic."

The four agentic design patterns

Reflection. The agent evaluates its own output and iterates. After generating a draft, it critiques the draft, identifies weaknesses, and revises. This produces higher-quality results than a single-pass generation.

Tool use. The agent calls external tools — search APIs, code interpreters, databases, web scrapers — to gather information and take actions that the LLM alone cannot. This is where CodeWords connects: its 500+ integrations serve as the tool library that agents use.

Planning. The agent decomposes a complex goal into a sequence of sub-tasks, determines dependencies between them, and executes in the right order. This requires the LLM to reason about task structure, not just content.

Multi-agent collaboration. Multiple specialized agents work together. A "researcher" agent gathers data, an "analyst" agent evaluates it, a "writer" agent produces the report. Each agent has different system prompts and tool access, creating a division of labor.

Agentic AI in automation platforms

The hype around agentic AI outpaces the reality. Most "AI agent" products are glorified prompt chains — a sequence of LLM calls with fixed tools. True agentic behavior requires dynamic planning, error recovery, and adaptive execution, which is genuinely hard to build reliably.

CodeWords takes a practical approach. Its workflows support agentic patterns — multi-step execution, tool use, conditional branching, and iterative refinement — within a structured framework. The FastAPI microservice architecture provides guard rails: Pydantic validation catches bad LLM outputs, Redis state persistence enables workflows that run across sessions, and ephemeral E2B sandboxes isolate each execution.

Platforms like Zapier and Make have added "AI agent" features, but their visual builder architecture limits how much autonomous behavior is possible. n8n supports agent patterns through its AI nodes. Wordware focuses on AI-native workflow definition. CodeWords provides the infrastructure for agentic workflows — LLM access, tool integrations, web scraping, and execution management — through a conversational interface backed by real code.

Real-world agentic workflow examples

Deep research agent. Describe a research question to CodeWords. The workflow searches multiple sources, scrapes relevant pages, synthesizes findings with an LLM, identifies gaps in the research, runs additional searches to fill those gaps, and compiles the final report. The iterative search-and-synthesis loop is the agentic behavior.

Lead qualification agent. A form submission triggers a CodeWords workflow that researches the company (web scraping, news search), scores the lead (LLM analysis against ideal customer profile), determines the best sales rep (based on territory, expertise, and availability), and routes accordingly via Slack and HubSpot.

Content monitoring agent. A scheduled workflow monitors competitor websites, detects changes (pricing updates, new features, blog posts), summarizes what changed, assesses strategic implications, and notifies the relevant team with actionable insights.

Limitations and guard rails

Agentic AI isn't magic. Agents can loop infinitely, hallucinate tool calls, or pursue unhelpful sub-tasks. Production-grade agentic workflows need: token and iteration budgets (stop after N steps), output validation at each step, human-in-the-loop checkpoints for high-stakes decisions, and comprehensive logging.

CodeWords provides these guard rails through its serverless architecture. Build agentic workflows at codewords.agemo.ai — start from a template and check pricing.