Agentic AI workflows

Agentic AI workflows are structured, goal-driven processes that coordinate one or more AI agents with enterprise systems to complete multi-step tasks through planning, execution, and adaptation. While an AI agent is an autonomous actor that can reason, decide, and use tools, an agentic workflow is the orchestration layer that defines how goals are represented, how state is tracked across steps, which agents are involved at each stage, how errors are handled, and when to stop or escalate. 

This distinction matters because workflows bring order, safety, and repeatability to agent logic, turning intelligent components into reliable enterprise processes that can run at scale under governance and human oversight.​ For enterprises, this shifts AI from a single interaction to an end-to-end workflow that reliably moves work forward across teams and applications.​

Agents vs agentic workflows

AI agents are autonomous software actors that can understand inputs, reason about options, and call tools to get things done. They behave like digital coworkers that can draft content, query systems, or trigger actions when prompted or when an event occurs. On their own, though, agents usually see only the current step, not the full business process they’re part of.​

Agentic workflows are the structure that surrounds those agents. A workflow defines the goal, the sequence of stages, how state is passed forward, what happens when errors occur, and where humans must review or approve results. It decides which agent should act when, which systems they are allowed to touch, and when the workflow should stop, retry, or escalate.​

A simple way to separate the two is to understand how they divide responsibilities:

• Focus: Agents focus on making a smart decision or action in a given moment, while workflows focus on getting the whole job done from start to finish.​
• Scope: Agents operate on a narrow task, but workflows span multiple steps, systems, and sometimes multiple teams.​
• Control: Agents optimize for autonomy, whereas workflows optimize for reliability, traceability, and alignment with business rules.​

For complex enterprise agentic AI use cases, the two work together. Agents provide flexible reasoning and tool use at each step, and agentic workflows provide the business process layer that keeps work reliable, auditable, and aligned with policies across applications and teams.​

Platforms and tools for agentic AI workflows

Agentic AI workflows need more than a language model and a few scripts. They run best on platforms that can coordinate many steps, survive failures, and connect reliably to the systems where work actually happens.​ In practice, most enterprise platforms for agentic workflows share a common set of building blocks.

Workflow and state orchestration: Enterprise workflows often run for long periods and span many services. A workflow engine with durable execution keeps track of progress so a process can pause, resume, or retry without losing state if something goes wrong. 

Orchestration tools (like Temporal) provide this foundation, ensuring workflows operate at scale in production rather than as short-lived demos. Graph-based frameworks add branching, looping, and human-in-the-loop pauses with full state persistence, making it easier to design workflows that adapt to intermediate results.​

Tool and system integrations: Agentic workflows create value when they can safely read and update data in the systems that run the business, such as CRM, ERP, ticketing, data warehouses, and industry platforms. 

Enterprise platforms address this with standardized connectors and integration patterns, including API based access, event-driven triggers, and secure data pipelines, so agents can call these systems as tools without one-off wiring for every workflow. Agentic AI workflow platforms for enterprise use rely on these shared integrations to plug into existing estates while keeping data access governed through common policies and role-based access control.

Agent and model coordination: Above the workflow engine, an AI layer manages prompts, context, and which agent or model handles each step. It routes tasks between planning, execution, and review agents and ensures they all see the right state at the right time. 

Frameworks designed for agent orchestration make it possible to define each agent’s behavior as a node, pass state between them, and control transitions based on success, failure, or human approval. This turns a collection of agents into a coherent process rather than isolated automations.​

Governance and observability: Enterprises need to know what workflows did, when, and why. Modern platforms capture audit logs, decision traces, and metrics, and support role-based access control and policy enforcement so only the right agents can call sensitive tools or data. Teams can insert human approval steps where needed, which is a key differentiator between top tools for automating agentic AI workflows and lightweight scripting approaches.​ This makes it possible to automate aggressively while still keeping humans accountable for the highest risk decisions.

In practice, leading platforms blend these capabilities rather than treating them in isolation. For example, an enterprise agentic AI platform that uses Temporal for durable workflows and adds agent orchestration, monitoring, and integration patterns can support use cases from financial investigations to AI-assisted software delivery within one consistent framework.​

Agentic AI workflows in practice

Agentic AI workflows become real when you see how they investigate issues, coordinate multiple agents, and adapt as conditions change. The examples below connect those patterns to how enterprises already use agentic AI in production.

Financial services: Investigative compliance workflows

A core pattern in financial services is a workflow that starts from a vague question and turns it into a structured investigation that gathers data, evaluates scenarios, and prepares a response. This is where agentic workflows shine, because they can coordinate multiple agents to expand the question, pull evidence from many systems, and keep every step auditable.​

Agentic AI regulatory compliance workflows follow this model: they take a natural language inquiry, expand it into concrete data needs, and then drive a multi-step investigation across trading, communication, and supervisory systems.

In practice, one agent interprets the inquiry and identifies relevant entities, dates, and regulations. Another agent maps that intent to specific data sources such as bitemporal trade records, email archives, and review logs. Additional agents retrieve and correlate this data, highlight suspicious patterns, and draft an explanation that a compliance officer can review and submit. The workflow coordinates these agents, tracks state across steps, and ensures every action is logged, mirroring the patterns used in advanced regulatory inquiry and remediation solutions for financial institutions.

Retail and customer-facing automation: Agentic commerce journeys

In retail, composable agentic AI workflows often sit behind customer experiences such as guided shopping, personalized promotions, or service journeys. A single customer request might require decisions across catalog, pricing, inventory, payments, and logistics systems.​

A typical retail automation workflow can:

• Use a conversational assistant agent to understand what the customer is trying to achieve.
• Call agents that search the catalog, evaluate compatibility, and check inventory and delivery options across locations.
• Simulate different bundles or offers based on business rules and customer history.
• Present a recommendation, and if accepted, trigger order creation, payment, and fulfillment steps through connected systems.​

Because the workflow keeps context across steps, it can adapt if an item is out of stock, if delivery windows change, or if the customer modifies preferences mid-conversation. This is more than a static script. It is an agentic commerce process that responds to real-time signals.

Industrial and operational applications: IoT control tower workflows

In industrial environments, a key pattern is a workflow that monitors live signals, investigates anomalies, and recommends or executes corrective actions. IoT control tower workflows for manufacturing and field operations embody this pattern by combining continuous data ingestion, anomaly detection, and agentic investigation.

Monitoring agents watch sensor streams, quality metrics, and equipment events from plants or fleets. When something looks off, a reasoning agent pulls in historical incidents, operational playbooks, and similar patterns to identify likely causes and remediation options. Additional agents simulate the impact of different responses, from adjusting process parameters to scheduling maintenance, and present recommendations to operators or automatically trigger approved actions. This approach underpins IoT control tower solutions that use agentic AI to reduce investigation time, improve response consistency, and standardize best practices across industrial sites.

Software engineering and internal productivity

Agentic workflows are also changing how software is built and maintained. In an agentic AI SDLC, workflows coordinate agents that understand requirements, generate code, run tests, and manage changes across repositories.​

A typical SDLC engineering workflow can:

• Take a user story from a ticketing system.
• Expand it into a technical plan and design changes across services and components.
• Generate code, configuration, and tests using coding agents connected to repositories and CI systems.
• Run test suites, interpret failures, propose fixes, and prepare pull requests for human review.​

This is not a single agent working in isolation. It is a network of agents working inside a structured workflow that preserves traceability, supports governance, and allows developers to supervise and steer outcomes rather than hand off full control.

In all these cases, agentic AI workflows are the layer that turns individual agents into reliable systems. They describe how agents should work together, when to ask for more data, when to stop, and when to involve a person. For enterprises that want to move beyond isolated pilots, designing these workflows is often the real work of operationalizing AI.