---
title: "What are Agentic Workflows? Definition, Examples & Cost"
description: "An agentic workflow is a multi-step procedure an AI runs end-to-end: picking tools, asking the right questions, finishing the job. Here's what good looks like."
url: https://myaskai.com/blog/what-are-agentic-workflows
---
# What are Agentic Workflows? Definition, Examples & Cost

Every vendor now sells "agentic AI", but most stop at the concept. Here's what an agentic workflow actually is, what good looks like in real customer-support rollouts, the per-step economics, and when you shouldn't build one.

> *An agentic workflow is a multi-step procedure an AI agent runs end-to-end — choosing which tools to call, asking the customer for missing information, and finishing the job without a human in the loop.*

In customer support, here's the difference it makes. A normal AI *answers* a refund question ("yes, your order is eligible, here's the policy"). An agentic workflow *handles* the refund: it looks up the order, checks the eligibility window, asks for the missing reason, [fires the refund through Stripe](https://myaskai.com/blog/best-ai-customer-service-crm-integration-2026), replies to the customer, and tags the ticket resolved.

The first is a single function call. The second is an agentic workflow. The simplest way we describe the line: a normal answer retrieves knowledge and hands it back in a tidier format, where an agentic workflow actually does the job a person would otherwise have done.

You're probably here to work out whether "agentic workflow" means anything different from "AI agent", "AI automation", or "what Zapier already does". It does. And the difference is worth understanding before you buy into it.

## Agentic workflows, in more depth

**TL;DR**: An agentic workflow runs a *defined* procedure but works out how to execute each step itself. Every one has four ingredients: a task definition, a scoped set of tools, a reasoning loop, and an exit condition.

An agentic workflow sits one notch above a single function call and one notch below a fully autonomous agent. It follows a *defined* procedure, but it works out *how* to run each step itself, using tools, memory, and plain reasoning instead of a fixed script.

Every agentic workflow, whoever builds it, has four ingredients:

1. **A task definition**: what the workflow is for, written in plain English or code ("process a refund when a customer asks and the order is eligible").
2. **A scoped set of tools**: the specific APIs it's allowed to call inside this workflow (look up an order, issue a refund, update an address).
3. **A reasoning loop**: the part that decides which tool to call when, and asks the customer for anything it's missing.
4. **An exit condition**: how it ends in success, escalation, or a refusal.

The term comes from Andrew Ng, who in 2024 reframed "agentic" as a *spectrum*. A system can be a little agentic or very agentic, depending on how much the model gets to decide for itself.

Breakdown of the four ingredients every agentic workflow has: a task definition, a scoped set of tools, a reasoning loop, and an exit condition.

The point [Ng made in 2024](https://x.com/AndrewYNg/status/1770897666702233815) is even clearer in 2026: how you wrap a model matters as much as which model you pick. The [agentic coding benchmarks people track today](https://www.datacamp.com/blog/gpt-5-5-vs-claude-opus-4-7), like SWE-bench and Terminal-Bench 2.0, run frontier models such as Claude Opus 4.7 and GPT-5.5 inside an agent harness, because the harness is what turns a capable model into a reliable one.

The cleanest boundary line comes from Anthropic's [Building Effective Agents](https://www.anthropic.com/research/building-effective-agents). It splits *workflows* ("LLMs and tools orchestrated through predefined code paths") from *agents* ("LLMs dynamically direct their own processes and tool usage"). The shorthand we use: with a workflow you own the plumbing, with an agent the model owns the plumbing.

An agentic workflow is the middle ground, where the model has real say over each step but the procedure and its exit are still yours.

So an agentic workflow is *not* the raw LLM call underneath it. It's *not* a prompt template, and it's *not* a Zapier-style trigger-to-action chain (those fire preset actions with no reasoning about which to use). It's also *not* a fully autonomous agent, which has no defined procedure at all.

The middle ground is the bit that's actually useful in support.

## How do agentic workflows work in practice?

**TL;DR**: A workflow fires on a trigger, then loops through tool calls and follow-up questions until it hits an exit (success, escalation, or refusal). A refund handled end-to-end is the textbook six-step example.

An agentic workflow fires when a customer message matches its trigger, runs a reasoning loop that calls tools and asks follow-up questions, then exits on success, escalation, or refusal. The easiest way to see it is to walk a single refund end-to-end, since that's the classic support example.

1. **Trigger.** A customer sends "I want a refund for order #4823." The AI matches this to the `process_refund` task definition, one of several tasks it has. Some vendors trigger on keyword or intent classifiers. Others let the model decide from the task descriptions themselves.
2. **Tool selection.** It reads the task definition, sees it needs the order details, picks the `lookup_order` tool from its allowed list, and calls it with `order_id=4823`.
3. **Follow-up question.** The order is found, but the reason field is empty. So it asks: "sure, can you tell me what's wrong with the item?" The customer replies "damaged on arrival", and it stores `reason=damaged`.
4. **Conditional logic.** It checks the eligibility rules: order placed within 30 days, item not on the no-refund list, reason in the auto-approved set. All pass.
5. **Action call.** It fires the `issue_refund` tool, which hits the Stripe API (or Shopify, or the helpdesk's own refund endpoint). Stripe returns success. The agent updates the ticket tag and adds an internal note.
6. **Exit and reply.** It posts the customer-facing message: "done, your refund of $47.50 is on its way and should land in 3 to 5 business days. Sorry about the damaged item." The ticket is marked resolved.

The mechanism is the same across vendors. What differs is what each one calls the surface you build the workflow on, and how you author it. Here's the variation across the platforms a support team is most likely comparing:

Six-step process flow of an agentic refund workflow: trigger, tool selection, follow-up question, conditional logic, action call, exit and reply.

| Vendor | What they call it | How it's defined | Notes |
| --- | --- | --- | --- |
| My AskAI | Tasks + Tools | Tasks are natural-language multi-step procedures; Tools are single API calls fired inside them | One agent runs many Tasks; defined in plain English, no flow-builder |
| [Fin (Intercom)](https://myaskai.com/blog/intercom-fin-ai-agent-complete-guide-2026) | Procedures | Natural-language multi-step instructions Fin follows and adapts in real time | Replaced Tasks (deprecated March 2026); visual "Workflows" handle linear flows |
| [Decagon](https://myaskai.com/blog/decagon-ai-complete-guide-2026) | Procedures | Agent-builder procedures the agent follows | Enterprise framing |
| Salesforce Agentforce | Topics + Actions | Topics are the task definitions, Actions are the tools; runs on the Atlas reasoning engine | The public face of the "agentic" branding push |
| Zendesk AI agents | Generative procedures + flow builder | Natural-language procedures for agentic AI, plus scripted conversation flows built in a builder | Still the most builder-heavy of the set; the legacy bot builder shuts off in December 2026 |
| Gorgias Automate | Automations / flows | Tool-style integrations; multi-step depth is shallower than the others on the current product surface | E-commerce-helpdesk native |

In our own product the two halves are [Tasks](https://support.myaskai.com/features/tasks) (the plain-English procedures) and [AI Actions, or Tools](https://support.myaskai.com/features/ai-actions-tools) (the single API calls fired inside them). I'll come back to how that works lower down.

**Designing one yourself?**Our guide to [AI support prompts, guidance and task workflows](https://myaskai.com/build-ai-support-agent/ai-support-agent-prompts-and-workflows) shows when an agent should answer, ask, follow a procedure or hand over. It is part of [our free 15-part playbook](https://myaskai.com/build-ai-support-agent).

## What's a "good" agentic workflow?

**TL;DR**: Judge a workflow on two numbers: how many of its triggered tickets it completes without a human (60 to 90% when it's working well) and the cost per run ($0.02 to $0.04 on per-step pricing). Your agent-wide resolution rate is a blunter, separate measure.

The benchmark for an agentic workflow is two narrow numbers: the *completion rate on the tickets the workflow triggers on* (of the ones it fired on, how many did it finish without a human?) and the *cost per run*. Your agent-wide resolution rate is a different measure, and a much blunter one.

There's no universal published benchmark for workflow completion. So the bands below are the range we see across customer rollouts (treat them as a rough guide):

| Tier | Completion on triggered tickets | What it usually means |
| --- | --- | --- |
| World-class | 75–90% | Tools wired correctly, eligibility logic well defined, under 5% hallucinated tool calls |
| Solid | 60–75% | One or two tools miss edge cases, but it escalates cleanly when unsure |
| Average | 40–60% | Either over-broad triggers (firing on tickets it shouldn't) or under-broad tools (can't finish without a missing API) |
| Needs work | <40% | Fires but escalates more often than not. The trigger or the tool set is wrong; redesign before scaling |

Here's the thing behind almost every successful rollout: completion jumps once the workflow can actually *finish* the job, which usually means wiring up the right tool. [Edel Optics](https://myaskai.com/blog/my-askai-edel-optics-case-study-2026) is the cleanest example. Their AI resolution went from around 25% to 79% after they added a User Data API that surfaced order, delivery, return, and tracking info, a lift of roughly 50 points.

[YouGarden](https://myaskai.com/blog/my-askai-yougarden-case-study-2026) tells the same story: 66% resolution (peaking near 82%) with a custom User Data API, across about 12,000 tickets a month, saving 965 hours every month. Both of those are [agent-wide resolution rates](https://myaskai.com/blog/ai-resolution-rate-benchmarks) rather than single-workflow completion rates. But they show what a workflow can do once the tool lets it complete the job instead of just describe the answer.

On cost, the unit that matters is the cost per run, and it swings a lot by pricing model:

- **$0.02–$0.04 per run**: a one-to-two-step workflow on our per-step pricing ($0.02 per Task step, including any tool call inside that step).
- $0.99 or more per run: a typical refund on per-resolution or per-outcome pricing (Fin charges $0.99 an outcome, Zendesk $1.50 to $2.00 per automated resolution).
- **$0.30 or more per run**: the trap of a workflow that loops, retries, or hallucinates extra tool calls. This is exactly why the per-step price matters before you scale.

We charge per step because the agent is doing real work on each one. It's replacing work a person would otherwise have done, whether that's looking something up, updating another system, or anything that takes someone's time. Value that at two cents a step and there isn't really a cheaper way to do it (I've run the maths a few times).

Three cost-per-run bands for an agentic workflow: $0.02 to $0.04 on per-step pricing, $0.30 or more when a per-step workflow loops or retries, and $0.99 or more on per-resolution pricing.

[Kriptomat](https://myaskai.com/blog/my-askai-kriptomat-case-study-2026) is the live counter-example to the per-outcome model. At 62% resolution, they turned down Intercom Fin at $0.99 per resolution as uneconomical. Their roadmap now runs through the User Data API into Tools and Tasks for identity-verification workflows.

## Common misconceptions about agentic workflows

**TL;DR**: Four myths trip teams up: that agentic means autonomous, that more tools make a workflow smarter, that you can let an agent loop until it finishes, and that every multi-turn chat needs a workflow at all.

### Misconception 1: an agentic workflow is the same as an autonomous agent

An agentic workflow has a defined procedure and an exit condition. A fully autonomous agent has neither. Mixing the two up is how teams get over-sold (a vendor pitches "fully autonomous AI" but ships scripted workflows) or under-deliver (a team buys a workflow tool expecting autonomy and feels let down).

Most vendor press in 2024 and 2025 used "agentic" to mean "anything past a single LLM call", which is where the confusion started. Anthropic's [workflow-versus-agent distinction](https://www.anthropic.com/research/building-effective-agents) is the authoritative fix.

### Misconception 2: more tools make a smarter workflow

The opposite tends to be true. As the tool list grows, the model gets *worse* at picking the right tool (counterintuitive, I know). That's documented on the [Berkeley Function-Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) and in work like ["Tool Preferences in Agentic LLMs are Unreliable"](https://arxiv.org/abs/2505.18135).

Anthropic's own advice is to keep tool sets small and well-documented. So the right move is to give each workflow its own small, scoped tool set. Resist the urge to hand one agent a giant shared toolset.

### Misconception 3: you can let the agent loop until it figures it out

Uncapped reasoning loops are the leading source of runaway cost and hallucinated tool calls. Every production framework now enforces step limits, cost limits, and a refuse-or-escalate exit (LangGraph's recursion limit and the max-iteration caps in assistant frameworks are both examples of the pattern).

There's a second requirement for anyone reviewing this on the security side: the workflow has to *log* every tool call and its result, so you have a full audit trail. You'll want it the first time something goes sideways.

### Misconception 4: anything that takes more than one reply needs a workflow

This is the one we see most, and none of the standard explainers mention it. Plenty of teams assume that any troubleshooting, or anything past a single back-and-forth, needs an agentic workflow. In reality you only need one when the job means going through several steps *and* calling other systems.

Most multi-turn troubleshooting is just the AI fetching knowledge and clarifying with the customer. That's ordinary Q&A, and building a task for it only adds per-step cost and failure surface for no gain.

The test we use is simple: does finishing this require calling another system? If the answer is no, it isn't a workflow. (In practice most businesses need somewhere between two and ten tasks, aimed at the ticket types that come up most often and resolve worst today.)

## What agentic workflows are NOT, and how they relate to adjacent terms

**TL;DR**: An agentic workflow is the *action* layer of an AI agent. It sits alongside RAG (knowledge), self-learning (improvement), and handoff (exit), and is distinct from a single tool call or a fixed Zapier automation.

Agentic workflow is one term in a cluster of related ones. The fastest way to pin it down is to see what it sits next to.

| Term | Definition | Difference from an agentic workflow |
| --- | --- | --- |
| AI agent | An LLM-driven system that perceives, decides, and acts over time | The agent *is* the runtime; an agentic workflow is a *procedure* the agent runs |
| Tool use / function calling | The mechanism that lets an LLM call an external API | A workflow *uses* tool calls; a single call by itself is one step, not a workflow |
| RAG | Retrieval-augmented generation, pulling relevant docs into the prompt | A workflow may *include* RAG as one step; RAG alone is read-only knowledge, not action |
| Deterministic automation (Zapier, Make, n8n) | A trigger then a fixed action chain, no LLM reasoning | Agentic workflows let the model choose which tool to call, not just fire preset actions |
| [AI-to-human handoff](https://myaskai.com/blog/what-is-ai-to-human-handoff) | The structured escalation when the AI can't continue | A workflow's exit condition may *be* a handoff; the handoff is a sub-protocol of the workflow |
| Self-learning AI | The loop that updates the AI's knowledge from human replies | An adjacent loop that improves what the workflow *knows*, not what it *does* |

A useful way to hold the whole cluster: agentic workflows are the *action* surface, RAG is the *knowledge* surface, self-learning is the *improvement* surface, and handoff is the *exit* surface. Together they make up the four sides of an AI customer-service agent's runtime.

## How does My AskAI handle agentic workflows?

**TL;DR**: My AskAI ships agentic workflows as Tasks (plain-English procedures) plus Tools (the API calls fired inside them), priced per step at $0.02. Edel Optics hit 79% resolution once the right Tool let its Tasks finish the job.

We offer agentic workflows through [Tasks and Tools](https://myaskai.com/features/tasks-and-tools): Tasks are natural-language multi-step procedures, and Tools are the single API calls fired inside them. A Task is written in plain English: "if the customer wants a refund and the order is within 30 days, look up the order, ask for the reason, and issue the refund through Stripe."

The AI picks the right Task per ticket, calls the right Tools inside it, and asks the customer for anything it's missing. There's no decision-tree chatbot to build (which, if you've ever built one, you'll know is a relief).

[Play video](https://www.youtube.com/watch?v=4o7ebwPrp8w)

The outcomes track the benchmark logic from earlier: the lift comes when the Task can finish the job. Edel Optics went from around 25% to 79% AI resolution after adding the User Data API that let its order-lookup Tasks resolve themselves, and YouGarden reached 66% (peaking near 82%) the same way. As their Head of Customer Service, Mamunur Rahman, put it:

> *"My AskAI has fundamentally changed how we support our customers. The quality and consistency of responses are extremely high, and it's allowed us to scale support without compromising the experience we're known for at YouGarden."* Full story: [how YouGarden hit 66% AI resolution, saving 965 hrs a month](https://myaskai.com/blog/my-askai-yougarden-case-study-2026).

Every native helpdesk AI now ships some version of multi-step workflows, so the honest question is where the real differences are. Three hold up. First, the Task is defined in plain English rather than a GUI bot-builder, which is what most natives hand you.

Second, Tasks are portable: move from Intercom to Zendesk and your Tasks come with you, where a native's workflows don't. Third is the pricing model, and it's the one I'd weigh hardest. We charge per step ($0.02, including any tool call inside the step) rather than per resolution or per outcome.

We don't have outcome-based pricing, and we deliberately counter it. Most of what drives a resolution rate up is work the customer does: connecting tools and APIs, improving knowledge, tuning guidance. The vendor's share of that lift is real but small.

Outcome-based pricing charges the customer for the gains their own work created; per-step pricing doesn't rise with it, so your cost per resolved ticket actually falls as your resolution rate climbs.

[Kriptomat](https://myaskai.com/blog/my-askai-kriptomat-case-study-2026) turning down Fin's $0.99 per resolution is that calculation playing out in real life.

One honest limit, stated plainly: Tasks are defined procedures, each with a fixed exit condition, so they stop short of open-ended autonomy. If you need an agent that runs with no defined procedure at all, that isn't what this is. (And given the cost and audit-trail issues in Misconception 3, for support work that's usually the right trade anyway.)

## FAQs

****What are agentic workflows in AI?****

An agentic workflow is a multi-step procedure an AI agent runs end-to-end: it picks which tools to call, asks for anything missing, and finishes the job without a human stepping in. In support, a refund handled start to finish (look up the order, check eligibility, issue the refund, reply) is the textbook case.

****What does "agentic" actually mean?****

It comes from Andrew Ng's 2024 framing of "agentic" as a spectrum instead of a label. A system can be slightly or very agentic, depending on how much say the model has over its own steps and tools. It's a more useful lens than arguing about whether something "is an agent", because almost everything useful sits somewhere in the middle.

****What are some examples of agentic workflows?****

In support, the common ones are refunds, order or delivery lookups (the "where is my order?" question), returns and RMAs, account or address updates, and KYC or identity checks. Each one needs the AI to call another system and act on what it finds, which is what makes it a workflow rather than a lookup. Edel Optics and Kriptomat are real rollouts built around these patterns.

****How do agentic workflows differ from traditional workflows?****

A traditional workflow (a Zapier or Make automation) follows a fixed path: this trigger always fires these actions in this order. An agentic workflow lets the model decide which tool to call and what to ask next, inside a defined procedure. Anthropic frames it as who owns the plumbing: in a traditional workflow you do, in an agentic one the model gets real say over each step.

****Is an agentic workflow the same as an AI agent?****

No. An AI agent is the runtime, the system that perceives, decides, and acts. An agentic workflow is a procedure that an agent runs. One agent usually runs several workflows (in our product, one agent runs many Tasks), picking whichever matches the ticket in front of it.

****What's the difference between a tool call and an agentic workflow?****

A tool call is a single action: one API request, like looking up an order. An agentic workflow strings several together, with reasoning and follow-up questions between them and an exit condition at the end. A tool call is a step; a workflow is the whole procedure that step belongs to.

****How do I build an agentic workflow?****

Define the task in plain English, scope the smallest set of tools it needs, set its exit conditions (success, escalate, refuse), test it on real past tickets, and cap its loops and cost before you scale. The selection question matters more than the build: look at the ticket types that come up most often and resolve worst today. In our experience most businesses end up with between two and ten tasks total.

****What are agentic document workflows?****

It's a narrower subtype where the steps work on documents: pulling fields from an invoice, checking them against a record, then filing or routing the result. The mechanism is identical to any other agentic workflow (definition, tools, reasoning loop, exit). The tools just happen to read and write documents.

****What are AI tasks vs agentic workflows?****

They're the same thing under two names: "agentic workflow" is the industry term, and "Task" is what we call the feature in My AskAI. If you found this page searching for "AI tasks" or "multi-step workflows", a Task is our version of an agentic workflow: a plain-English procedure the AI runs using Tools.

****Do agentic workflows replace human agents?****

Not the way the marketing implies. They take over the repetitive, system-touching jobs (refunds, lookups, updates) so your people handle the conversations that need a person. The point is to stop your team doing the two-cents-a-step work a procedure can do, and hand back the time the unresolved tickets were eating.

- Moving to AI First Support ### [What is Self-Learning AI? A Plain-English Definition for Customer Service](https://myaskai.com/blog/what-is-self-learning-ai) Self-learning AI is an AI agent that updates its own knowledge from new tickets and human replies. How it works in customer service, and what good looks like. 22 May 2026  17 min read
- Moving to AI First Support ### [What is AI-to-human handoff? Definition, triggers, and what "good" looks like](https://myaskai.com/blog/what-is-ai-to-human-handoff) AI-to-human handoff is the structured transfer of a conversation from an AI agent to a human. Here's when it fires, how it works, and how to spot a bad one. 28 May 2026  22 min read
- Glossary ### [What is Historical Ticket Training? Definition, How It Works, and What to Avoid](https://myaskai.com/blog/what-is-historical-ticket-training) Historical ticket training teaches AI support agents from past resolved tickets. How it works, vendor differences, benchmarks, and what most guides get wrong. 20 May 2026  18 min read
- Moving to AI First Support ### [What are AI hallucinations? Definition, causes, and how to spot them](https://myaskai.com/blog/what-is-ai-hallucination) AI hallucination is when an AI states something false with confidence. Here's why it happens, real examples, and how customer-support AI prevents it. 26 May 2026  19 min read
- Moving to AI First Support ### [What is Outcome-Based Pricing? Definition, Examples, and How AI Vendors Bill For It](https://myaskai.com/blog/what-is-outcome-based-pricing) Outcome-based pricing charges for results, not seats. Here's how AI vendors count outcomes, where the model breaks, and the usage-based alternative. 27 May 2026  20 min read
- Alternatives ### [Intercom Fin AI: Complete Guide to Features, Pricing & Limitations (2026)](https://myaskai.com/blog/intercom-fin-ai-agent-complete-guide-2026) Fin AI has resolved 40m+ conversations, but costs $0.99 each. My AskAI starts at $0.10 a ticket. Reddit users say bills get 'expensive fast'. 3 Mar 2026  20 min read

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