How to build agentic workflows: 5 tips for success

Agentic workflows stand to change the way your business runs. While advances in automation helped reduce repetitive, time-consuming work, agentic workflows can remove this work from your plate entirely. Instead of automating a single task (like sending a notification when a task status is updated), agentic workflows enable AI agents to reason, plan, and execute across multi-step processes (orchestrating work across an entire project plan, for example). The work is always-on, and built and monitored by your team in the way that works best for your business. 

Here's what agentic workflows are, how they work, and five practical tips for building them so that they work for your workflows.

What are agentic workflows?

An agentic workflow is a process in which one or more AI agents take on the planning and execution of a sequence of tasks with minimal human intervention. 

Agentic workflows represent an evolution of traditional, task-based automation, which follows defined rules. (If X happens, then Y happens next.) In agentic workflows, AI agents can break down a goal into subtasks, decide how to approach each one, use tools to access information or take an action, and adjust their approach based on what they find. They behave more like a teammate working toward a shared objective. For example, you might follow an AI play from Airtable to build a workflow that analyzes product feedback, or one that localizes images and marketing copy at scale.

How do agentic workflows work?

Agentic workflows are powered by large language models (LLMs) that allow agents to reason and take action toward an end goal. The key here is that agents have ‘agency’ (or the ability) to make their own decisions, which are also influenced by the context that surrounds the core model. These are things like agent memory, skills, and the tools and proprietary data that the agents can access. 

A typical agentic workflow includes these phases:

It’s important to understand that the agent isn’t pre-programmed and is making real-time decisions based on the information it has in the moment.

Components of agentic workflows

Successful agentic workflows depend on the following components, working together:

AI agents

An agent or agents sit at the core of any agentic workflow. Agents can be general-purpose or specialized for a specific function (research, summarization, data extraction, outreach, and so on). In more complex workflows that involve multiple agents working together, each agent handles one specific task.

Memory

Agents need context to work well, and without memory, agents have to reason through each task from scratch, even if it’s something they’ve done before. Short-term memory gives an agent the context of the current task. Long-term memory lets it draw on past interactions, decisions, or learned preferences.

Tools and integrations

Agents take action through tools: web search, APIs, databases, code execution, and more. The tools available to an agent define what it can actually do. An agent with access to your CRM, your email system, and your project data can accomplish far more than one operating in isolation. It’s important to define, however, what an agent can simply read (view) and where it can write (make changes).

Orchestration layer

In multi-agent systems, an orchestration layer manages how agents communicate and coordinate. It determines task sequencing, routes outputs between agents, and handles exceptions when something doesn't go as planned. 

Human-in-the-loop controls

Even the most autonomous workflows need defined points where humans can review, approve, or intervene. Human-in-the-loop design safeguards your operations and helps your workflows improve over time.

How to build agentic workflows

You can build production-ready agentic workflows using these five steps.

1. Start with a well-defined goal

Identify a workflow where an agent (or agents) can add value. Generally, these are repetitive and time-consuming tasks. Then, take the time to be specific about what success looks like before you build an agent and set it loose. What does the agent need to accomplish? What does a good output look like? What should it do when it encounters an edge case?

Vague objectives produce vague results, so the more precisely you can define the goal, the more reliably the agent will pursue it. Think of it as writing a job description: the clearer the scope, the better the hire.

2. Give agents the right data context

Agents are only as good as the context they can reason across. If an agent has to guess at missing information, it will — and it won't always guess correctly. Make sure your agents have access to the operational data that's relevant to their task: past decisions, current status, relevant records.

This is where structured data matters. Agents working from unstructured documents or disconnected systems will hit limits, but when workflows live in a single platform with structured, organized data, agents can access the same context as your team(s).

3. Design for observability from the start

One of the most common mistakes in early agentic deployments is treating agent observability as an afterthought. If you can't see what an agent is doing and why, you can't trust its decisions or improve its outputs.

Build in visibility by choosing a system that allows you to log agent decisions, actions, and outputs. Ideally, your solution should include an accessible interface where you can create dashboards for your team (whether technical or not) to monitor. Define what good looks like, but also what "off track" looks like so that you can catch it early.

4. Use modular, composable design

It’s generally not a good idea to build one agent to handle a whole range of tasks. Instead, modular design — where each agent handles a specific function with focus and passes outputs to the next — is easier to build, test, and debug if something goes wrong.

Think in terms of agent "skills," discrete capabilities that can be combined in different ways depending on the workflow. These skills might be: researching, analyzing data, or actually creating content based on the data. Modular design also helps you scale naturally, so that you build core capabilities and add new ones as you grow.

5. Build in human checkpoints

While AI agents add value to operational workflows, human oversight is also valuable. Consider which parts of the workflow benefit from a human check-in or approval to ensure that the agent doesn’t make a decision that might negatively impact the business or customer.

This is called human-in-the-loop design, which helps build trust in your workflows and improves them over time.  

The future of agentic workflows 

Airtable found that 56% of organizations surveyed are still in the earliest stages of human-agent collaboration. Most organizations are running individual agents on narrowly scoped tasks — a research agent here, a drafting agent there — or as personal productivity tool. The near-term shift is toward coordinated multi-agent systems: networks of specialized agents that collaborate across more complex, long-running processes.

This is not likely to be a slow shift towards more complex workflows. In fact, Gartner expects enterprises to “abandon” assistive AI by 2028, in favor of platforms that support outcome-focused workflows that benefit the wider team and business. Agentic workflows will be core to your operational model, and the teams that are pulling ahead are building the systems, the data structures, and the governance layers that agents need to work reliably at scale.

Airtable: a home for your agentic workflows

It matters where you build your agentic workflows. Building ones that run reliably in production, where humans and agents can work side by side with full visibility, requires the right foundation. Airtable is designed for exactly this, providing teams a shared operational surface where agents can reason across structured data and everyone can see what the agents do, and why. Whether you're running your first agent or coordinating dozens across your teams, Airtable provides an

agent governance

layer that helps improve your workflows and keep your operations running smoothly.