What are agentic workloads?

3 days ago

6 min read

Agentic workloads are the portions of an organization's work that are assigned to AI agents to carry out, with the agent planning steps, using tools, and producing results inside a workflow that humans remain accountable for.

In infrastructure language, an agentic workload is the computing demand that AI agents generate. In work management language, it is the work itself: the tasks, decisions, and handoffs an agent now performs. The chip industry is measuring how much agentic work is running. Work management asks whether that work is clear, owned, and producing value.

Two meanings of "agentic workloads"

The term is used in two fields, and both are correct. They describe the same phenomenon from opposite ends.

	Infrastructure meaning	Work management meaning
What a workload is	A compute job running on CPUs, GPUs, and memory	A body of organizational work assigned to a performer
Who uses the term	Chipmakers, cloud providers, data center operators	Operations leaders, workflow architects, work managers
Core question	How much compute do agents consume, and how do we serve it efficiently?	What work have we delegated to agents, and is it producing outcomes?
Unit of measure	Tokens, inference calls, CPU and GPU hours	Tasks, handoffs, cycle time, rework, outcomes
What failure looks like	Latency, cost overruns, capacity shortages	Unclear ownership, drift, rework, activity without value

Every agentic workload on a server rack starts as a decision someone made about work. When an organization delegates invoice matching or ticket triage to an agent, the infrastructure team sees a new compute load. The work management question is upstream of it: was that delegation designed, owned, and measured?

Why the term is everywhere now

Agentic workloads became a headline term in fall 2026 because chipmakers started reporting them as a primary driver of demand.

AMD Q3 2026 earnings (Nov 3, 2026): CEO Lisa Su reported data center revenue up 97% year over year to a record $8.5 billion, with agentic workloads contributing significant growth. She described enterprises putting agents to work across software development, supply chain, and IT operations. (transcript)
OCP Global Summit (Oct 2026): Su said inference could reach about 60% of the AI market as agentic workloads drive compute demand, and introduced Helion, a rack system built for agentic workloads. (Yahoo Finance)
CPU demand: Su has said agentic AI pushes CPU-to-GPU ratios toward better than 1:1, a sign that agent work involves far more than running models: orchestrating steps, calling tools, and moving data. (Stocktwits/CNBC)

That last point is the bridge to work management. When hardware has to be redesigned because agents spend their time coordinating, coordination has become a measurable cost of AI. Work management is the discipline that decides what that coordination is for.

Agentic workloads vs. agentic workflows vs. automation

An agentic workflow is the designed path work follows; an agentic workload is the volume of work actually flowing through it to agents.

	Automation	Agentic workflow	Agentic workload
What it is	Rules-based steps executed the same way every time	A workflow where AI agents act as participants alongside humans	The body of work an organization has delegated to agents
Answers the question	What runs automatically?	How is human-agent work designed?	How much work have we handed to agents, and what is it producing?
Judgment	None; follows predefined logic	Agents plan and choose steps within designed boundaries	Varies by task; set by the delegation decision
Owned by	Process or system owner	Workflow Architect	Signal Owner and workflow owner, per the IDEAS model
Managed with	Triggers, rules, error handling	Agentic Workflow Architecture™, the 7 WA Standards	AI Workflow Governance™, WPIs™, capacity planning

Put simply, the workflow is the architecture and the workload is the traffic. Organizations that track only the traffic end up measuring how busy their agents are, not whether they are useful.

What makes a workload agentic

A workload is agentic when the agent, not a script, decides how to get from the request to the result. Four characteristics separate it from ordinary automation:

Goal-directed: the agent receives an outcome to reach, not a fixed sequence of steps.
Multi-step and tool-using: it plans, calls systems, retrieves information, and revises its approach along the way.
Variable effort: the same type of request can take two steps or twenty, which makes cost and cycle time harder to predict.
Handoff-bound: it starts from a human or system handoff and ends in one, so its value depends on the clarity of both.

Examples of agentic workloads

Function	Agentic workload	The work management question
Finance	Matching invoices to purchase orders and drafting exception notes	Who approves exceptions, and within what window?
IT operations	Triaging incoming tickets, gathering diagnostics, and routing them	Is the handoff to a human engineer explicit and complete?
Software development	Writing, testing, and opening pull requests for routine fixes	Who reviews, and how is rework tracked?
Supply chain	Monitoring supplier signals and proposing reorder adjustments	Which decisions can the agent make, and which only recommend?
Marketing	Producing first drafts of campaign content and variants	What reference material defines "on brand," and who checks drift?

In each case the agent does what was once the "first job" of the function. The human role shifts to the second job: designing, assigning, and checking the work.

How to manage agentic workloads

Agentic workloads are managed like any other work: someone owns the intent, the design, the execution, and the signal. What changes is that the performer is an agent, so delegation and drift must be made explicit.

1. Assign ownership with the IDEAS model. Every agentic workload needs named owners across Intent, Design, Execution, Alignment, and Signal. The agent can perform Execution; a human must own the rest. See the IDEAS Workflow Ownership Model.

2. Govern with AI Workflow Governance™. Its three components map directly onto agent work:

Explicit Delegation: state what the agent may decide, what it may only recommend, and where it must hand back to a human.
Reference Alignment: give the agent the source material that defines correct output, such as policies, standards, and examples.
Drift Detection: check output against that reference over time, since agent behavior shifts as inputs and models change.

3. Measure outcomes with WPIs™, not token counts. Compute metrics show how much agents are running. Workflow Performance Indicators show whether the work is flowing: cycle time and wait time (Flow), rework and clarification requests (Quality), and variation (Stability). Each WPI needs a Signal Owner.

4. Design the handoffs. Most agentic failures happen at the edges, where work enters or leaves the agent. The Explicit Handoffs standard of Workflow Architecture applies to agent-to-human and human-to-agent transitions exactly as it does between people.

The infrastructure industry is optimizing how cheaply agentic workloads run. The work management task is making sure they are worth running. This is the same argument WMI made in Tokenmaxxing vs. Valuemaxxing.

Agentic workloads and maturity

How much agentic work an organization can absorb depends on its Human-AI Workflow Collaboration Maturity™. Here is what agentic workloads tend to look like at each of the five levels:

Isolated AI Assistance: individuals use AI on their own tasks; there is no organizational agentic workload to speak of.
Informal Integration: agents appear inside team work, but delegation is implicit and nobody owns the results.
Structured Participation: agentic workloads are designed in, with explicit delegation and named owners. This is the threshold for scaling them safely.
Integrated Multi-Agent: several agents hand work to one another as well as to people; handoff standards become critical.
Adaptive Multi-Agent: agentic workloads are monitored and redesigned continuously based on performance signals.

Compute spending can jump an organization from Level 1 to Level 4 activity overnight. It cannot skip the ownership and design work of Level 3.

Frequently asked questions

What is an example of an agentic workload? An AI agent that triages incoming IT tickets, gathers diagnostics, and routes each ticket to the right engineer is an agentic workload. The agent decides the steps; a human owns the routing rules and the outcome.

Are agentic workloads the same as agentic workflows? No. An agentic workflow is the designed structure in which humans and agents work together. Agentic workloads are the work that flows through that structure to agents.

Why do chipmakers talk about agentic workloads? Because agents generate sustained compute demand. They run many steps per request and raise demand for CPUs as well as GPUs, so companies like AMD now report them as a growth driver.

Who should own an agentic workload? A human. The agent can execute the work, but intent, design, alignment, and signal ownership stay with people, as set out in the IDEAS model.

How do you measure agentic workloads? Measure outcomes, not activity. Track cycle time, wait time, rework, and variation through Workflow Performance Indicators rather than token or call volume alone.

Sources

AMD Q3 FY2026 earnings call transcript, Yahoo Finance, Nov 3, 2026
AMD CEO Lisa Su Sees AI Inference Boom Fueling $1.5T Demand By 2030, Yahoo Finance, Oct 2026
Lisa Su Says Customers Want 'A Lot More Compute', Stocktwits, Aug 2026

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