# Agentic Workflow

AI Glossary · Agents & Automation
Canonical URL: https://aisat.uk/glossary/agentic-workflow
Author: Lucy Gilmore, Assistant General Counsel, Product, Privacy & AI at Sprinklr | Practical Privacy, Product & AI Governance & Defensible Risk Decisions (https://www.linkedin.com/in/lucy-gilmore/)
Breadcrumb: [Glossary](https://aisat.uk/glossary) > [Agents](https://aisat.uk/glossary?category=agents) > Agentic Workflow

## TL;DR

> **An agentic workflow** is a structured, multi-step process in which language models, deterministic code, and external tools collaborate to complete a task. Unlike an open-ended autonomous agent that invents its own control flow, an agentic workflow follows an engineered graph of routing, reflection, and verification steps.

## How Agentic Workflow works in practice

Most reliable production systems labelled as agents are actually agentic workflows. Rather than handing a model a goal and twenty tools in an unconstrained loop, engineers decompose the business process into explicit nodes and edges using orchestration frameworks or state machines. Deterministic code handles control flow, retries, and schema validation, while language models are invoked at specific nodes that require judgement, synthesis, or unstructured data extraction.

Common design patterns inside an agentic workflow include prompt chaining (breaking a task into sequential stages), routing (classifying an input and sending it to a specialised sub-pipeline), parallelisation (running multiple retrieval or evaluation passes concurrently), and evaluator-optimiser loops (where a second model call critiques a draft against a rubric and triggers a revision if checks fail).

Consider a Bristol insurance claims processor handling commercial property claims. The workflow first runs an OCR and extraction node over uploaded loss-adjuster PDFs, routes claims above £25,000 to a policy-exclusion check against the underwriting database, runs a critic node to verify every cited policy clause exists verbatim, and outputs a structured JSON dossier to the claims handler's queue.

## Why it matters and trade-offs

Agentic workflows give engineering teams the capability gains of iterative AI reasoning without sacrificing production observability or budget predictability. Because the state graph is fixed in code, teams can unit-test individual nodes, set hard caps on loop iterations, mix cheap small language models for classification with frontier reasoning models for synthesis, and satisfy compliance auditors who demand explainable decision paths.

The trade-off is upfront engineering effort and lower flexibility on edge cases that fall outside the pre-designed graph. If the task domain is highly unstructured, such as exploratory codebase debugging or open-ended market research, a rigid workflow will brittlely fail on unanticipated states, making a more dynamic AI agent with tool-selection freedom the better fit.

## Related AI concepts

- [Agentic AI](https://aisat.uk/glossary/agentic-ai): Agentic AI refers to artificial intelligence systems engineered to pursue multi-step objectives with bounded autonomy by decomposing goals, invoking external tools, inspecting intermediate results and adapting plans under defined governance controls.
- [AI Agent](https://aisat.uk/glossary/ai-agent): An AI Agent is a software system powered by a large language model that perceives environment context, maintains state, formulates multi-step plans and executes actions across external tools, APIs and databases to complete a delegated goal.
- [Function Calling (Tool Use)](https://aisat.uk/glossary/function-calling): Function Calling (Tool Use) is a model capability in which a language model inspects declared tool schemas and emits a structured JSON invocation naming an external function and arguments for the host runtime to execute.
- [Evals (Evaluations)](https://aisat.uk/glossary/evals): Evals (Evaluations) are automated, repeatable testing pipelines that score an AI application's outputs against a golden dataset using deterministic assertions, reference metrics and LLM-as-a-judge rubrics before any prompt or model change ships.