# Agentic workflow

An agentic workflow is a process where an AI agent breaks a goal into steps, takes action, checks its own work, and adjusts until the task is done.

## What is an agentic workflow?

An agentic workflow is a way of working where an AI agent plans, acts, and reviews its own results without a person driving every step. The agent splits a goal into smaller tasks, calls tools to do the work, checks what comes back, and keeps going until it finishes.

A chatbot answers one prompt at a time, and a fixed script repeats the same steps on every run. The agentic approach works differently, because the agent chooses its next step based on what it learns along the way.

This approach is spreading fast. According to McKinsey's 2025 State of AI survey[McKinsey's 2025 State of AI survey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai), 62% of organizations are at least experimenting with AI agents.

## Key characteristics

• - **Autonomy:** The agent decides its next step instead of following a fixed script.
• - **Planning and reflection:** The agent splits a goal into sub-tasks, reviews each result, and iterates through a plan, act, reflect, and synthesize cycle.
• - **Tool use:** The agent calls external tools, like a search API or an extraction tool, to pull live data from the web.
• - **Orchestration:** An orchestration loop, sometimes called an agent harness, coordinates the steps, tool calls, and hand-offs into one end-to-end run.
• - **Grounded, cited output:** Strong workflows attach a citation and a confidence score to each result, so you can trust the answer.

## Example

Say you ask a research agent to find the top five competitors to Company X and summarize their pricing. The agent plans a set of sub-queries, searches the web, extracts the relevant pages, and synthesizes a cited summary in minutes.

Parallel's Task API[Task API](/products/task) runs this same plan, search, reason, and report loop. It returns structured output with a citation and confidence score for each field.

_Parallel's benchmarks show how its APIs compare on accuracy, latency, and cost for agentic web research. Source: Parallel benchmarks[Parallel benchmarks](/benchmarks)._

## Related terms

• - what is an AI agent[what is an AI agent](/articles/what-is-an-ai-agent)
• - AI deep research[AI deep research](/articles/what-is-deep-research)
• - web search API[web search API](/articles/what-is-a-web-search-api)
• - semantic search[semantic search](/articles/what-is-semantic-search)
• - agent harness[agent harness](/articles/what-is-an-agent-harness)

## FAQ

**How is an agentic workflow different from a chatbot?**

A chatbot responds to one message at a time and waits for you to guide it, while an agentic workflow plans and completes a multi-step goal on its own.

**How is an agentic workflow different from traditional automation?**

Traditional automation follows fixed rules that a person writes in advance, while an agent decides its own steps and adapts when the results change.

**Do agentic workflows still need people?**

Yes, people still set the goals, review the output, and manage risk. Gartner predicts[Gartner predicts](https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027) over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls.