What Are Agentic Workflows? How AI Agents Execute Multi-Step Tasks
Most automation tools follow a fixed script: if X happens, do Y. Agentic workflows are different. Instead of following a predefined sequence, an AI agent reads a goal, breaks it into steps, decides what to do at each step, and adjusts when something unexpected happens.
This isn't a minor upgrade to traditional automation. It changes what's possible — from "automate this exact process" to "achieve this outcome."
This guide covers what agentic workflows are, how they differ from standard automation, where they're being used in production, and what's required to actually build one.
What Is an Agentic Workflow?
An agentic workflow is a sequence of tasks executed by one or more AI agents that can perceive context, make decisions, use tools, and iterate — without requiring a human to approve each step.
The word "agentic" comes from agency — the capacity to act independently. In the context of AI, an agent is a system that can:
- Receive a goal or instruction
- Plan the steps needed to reach it
- Execute those steps using available tools
- Observe the results and adjust its approach
- Decide when the goal is achieved (or when to escalate)
A workflow becomes agentic when an AI agent is driving that sequence rather than a human-authored script.
A simple example: A traditional automation workflow might look like: "Every Monday, pull the sales report from Salesforce, format it, and email it to the team." Every step is hardcoded. If the report format changes, the automation breaks.
An agentic version of the same task might look like: "Prepare a weekly sales summary for the leadership team." The agent figures out where to get the data, how to structure the summary based on what's most relevant this week, and how to send it — and it adapts if the data source or format changes.
Agentic Workflows vs. Traditional Automation
| Traditional Automation | Agentic Workflows | |
|---|---|---|
| Logic | Fixed, rule-based | Dynamic, goal-based |
| Error handling | Fails or routes to fallback | Observes, retries, adjusts |
| Multi-step reasoning | Pre-scripted | Runtime decision |
| Tool use | Configured in advance | Chosen based on context |
| Human involvement | Define every step | Define the goal |
| Handles edge cases | Only ones explicitly coded | Can reason about novel situations |
The tradeoff: agentic workflows are more flexible but less predictable. The output of a fixed automation is deterministic. The output of an agentic workflow depends on how the agent reasons about the goal.
Core Components of an Agentic Workflow
1. The agent (or agents)
The AI model that reads goals, reasons about them, and decides what to do. Most production agentic workflows use a large language model (LLM) as the reasoning core, with tools attached.
2. Tools and integrations
Agents need to be able to do things, not just think. Tools give them the ability to: search the web, query a database, call an API, read or write files, send a message, fill out a form. The range of available tools determines what the agent can actually accomplish.
3. Memory
For multi-step tasks that span time or context windows, agents need a way to remember what they've done and learned. This can be short-term (conversation context), long-term (a database of past interactions), or episodic (a log of this specific task's steps).
4. Orchestration layer
For workflows involving multiple agents, something has to coordinate who does what. An orchestration layer routes subtasks to the right agent, aggregates results, and handles failures.
5. Human-in-the-loop gates (optional)
Not every agentic workflow runs fully autonomously. Some designs include checkpoints where a human reviews the agent's plan or output before it proceeds. These are most common in high-stakes workflows (financial transactions, customer communications, legal review).
Types of Agentic Workflows
Single-agent workflows
One agent handles the full task. Best for contained, well-scoped goals where the steps are related and the required tools are limited. Example: an agent that researches a company, summarizes the findings, and drafts an outreach email.
Multi-agent workflows
Different agents specialize in different tasks. A coordinator agent breaks the goal into subtasks and routes them; specialist agents execute. Better for complex goals that require different capabilities. Example: a research agent that gathers data, a writing agent that drafts content, and a review agent that checks for accuracy.
Human-in-the-loop workflows
Fully autonomous for most steps, but with defined checkpoints where a human approves before the agent proceeds. Example: an agent that processes expense reports autonomously but flags anything over $5,000 for manager approval.
Reactive workflows
Triggered by an event rather than a direct instruction. The agent monitors a condition and acts when it's met. Example: an agent that watches a competitor's pricing page and alerts the team when a change is detected.
Where Agentic Workflows Are Being Used
Customer operations
Agents that handle support tickets end-to-end: reading the complaint, checking account status in the CRM, identifying the issue, issuing a credit or routing to the right team — without a human touching every ticket.
Sales and revenue
Agents that research prospects, pull relevant context from previous calls, draft personalized outreach, and update the CRM — compressing hours of prep work into minutes.
Finance and accounting
Agents that reconcile transactions, match invoices to POs, flag discrepancies, and prepare reports for review. Many accounting workflows are high-volume and repetitive — well-suited to agentic handling.
Data operations
Agents that pull data from multiple systems, clean and normalize it, run analysis, and surface findings — replacing brittle ETL pipelines with reasoning-capable processes that adapt to schema changes.
Software development
Agents that read a bug report, reproduce the issue, write a fix, run tests, and open a PR. Still early, but increasingly in production at teams willing to tolerate imperfect outputs and review before merging.
What Makes an Agentic Workflow Reliable
Flexibility is the appeal; reliability is the challenge. An agent that can reason about novel situations can also reason its way into novel mistakes.
Clear goal specification
Agents perform best when the goal is specific. "Improve our customer retention" is too vague. "Identify customers who haven't logged in for 30 days and draft a re-engagement email for each one" is workable.
Scoped tool access
Give agents access to what they need and nothing more. An agent that can read from a database doesn't need write access unless the task requires it. Narrow permissions limit the blast radius of mistakes.
Observability
You need to be able to see what the agent did at each step. Good agentic systems log the agent's reasoning, the tools it called, and the results it received — so when something goes wrong, you can trace exactly where and why.
Fallback and escalation paths
Agents fail. The question is what happens when they do. Well-designed agentic workflows define what the agent should do when it's stuck (retry, escalate to a human, stop and report), rather than letting it loop or silently produce bad output.
The System Access Problem
Agentic workflows are most valuable when agents can act across the systems your business actually runs on. That's where things get complicated.
Many enterprise systems — HR platforms, property management tools, utility portals, financial systems — don't have public APIs. They're designed for human users, accessed through web interfaces with logins, session timeouts, and sometimes multi-factor authentication.
Standard automation tools can't reach these systems without API access. Agentic workflows that rely on API calls hit the same wall.
Deck is built for this problem. Instead of requiring an API, Deck agents interact with web portals the same way a human would — navigating the UI, logging in securely via Deck Vault (which handles MFA automatically), and extracting or entering data directly. This extends agentic workflows to systems that were previously off-limits.
Building Agentic Workflows: What You Actually Need
1. An LLM with strong instruction-following
The reasoning quality of the underlying model determines how reliably the agent executes. Models with strong function-calling support (where the model can natively invoke tools) outperform prompt-hacking approaches.
2. A tool layer
Define what actions the agent can take. Each tool needs a clear description so the model knows when to use it. Poorly documented tools lead to agents calling the wrong thing in the right situation.
3. A testing framework
Agentic workflows are harder to unit test than traditional code because the path through the workflow isn't fixed. Test with diverse inputs, log everything, and compare outputs against expected results. Treat evals as a core part of the build process, not an afterthought.
4. Human review during rollout
Run the workflow with human oversight before going fully autonomous. This surfaces failure modes you didn't anticipate and builds confidence in the agent's behavior before it's acting unsupervised on real data.
FAQ
What's the difference between an agentic workflow and a simple AI chatbot?
A chatbot responds to individual messages in a conversation. An agentic workflow executes multi-step tasks autonomously — it doesn't just answer questions, it takes actions: calling tools, making decisions, and iterating toward a goal.
Do agentic workflows require coding to set up?
It depends on the platform. Building a custom agentic system from scratch requires engineering work. Platforms like Deck let non-technical teams configure agentic workflows through a UI, connecting to tools and systems without writing code.
How do agentic workflows handle mistakes?
The right answer is: with defined fallback paths and human escalation options. Agents make errors. Robust agentic systems log what happened, stop when they hit a dead end, and surface the issue rather than proceeding on bad assumptions.
Can one agentic workflow span multiple software systems?
Yes — and that's where most of the value is. A single agentic workflow might pull data from a CRM, update a record in an accounting system, and send a notification in Slack. The agent coordinates across tools that don't natively talk to each other.
What systems can agentic workflows connect to?
Any system with an API — and with tools like Deck, systems that only have a web interface. The limiting factor is usually authentication and session management, not the web interface itself.
Are agentic workflows the same as RPA?
No. RPA (robotic process automation) follows fixed scripts to interact with UIs. Agentic workflows use AI reasoning to decide how to accomplish a goal, adapting when the UI or data changes. Agentic workflows are more resilient and capable but require more careful design.