Why Agentic AI Workloads Feel Different
If you have spent any time around enterprise AI deployments in the past eighteen months, you have probably noticed a shift. Early machine learning pipelines were predictable: you fed in data, trained a model, and served predictions. The whole loop was batch-oriented or request-response. That world is still here, but something new is growing alongside it. Agentic AI workloads are becoming common, and they do not behave like the models we are used to.
An agentic workload is not just a model call. It is a loop where an AI system reasons, acts, and adjusts based on what it finds. Think of a customer service agent that books a flight, checks seat availability, and then rebooks when the airline changes gates. That agent might call a dozen APIs, read a PDF, and decide to escalate to a human. The compute pattern is bursty, stateful, and hard to predict. That changes how you plan infrastructure.
What Makes Agentic Workloads Hard
The first challenge is state. Traditional inference servers are stateless. You send a prompt, you get a response, and you forget about it. Agentic systems hold context across multiple turns. They remember previous actions, results, and partial plans. That memory lives in the application layer, not the model, which means you need a way to persist and retrieve state fast. If your agent pauses for ten seconds to load context, the user experience degrades.
The second challenge is branching. An agent might decide to search a database, then call an external API, then re-prompt the model with new information. Each branch introduces variable latency. Some branches finish in two hundred milliseconds. Others take four seconds because the external service is slow. You cannot just scale up GPU instances and call it solved. The bottleneck shifts from compute to I/O and orchestration.
The third challenge is debugging. When a traditional model gives a wrong answer, you can trace the input and retrain. With an agent, the error might come from a bad API call, a stale context, or a prompt that led the model down a bad path. Finding the root cause requires observability into each step. Logging every action is table stakes, but correlating those logs across distributed services is still hard.
Practical Approaches That Work
I have seen teams try two broad strategies for handling agentic AI workloads. The first is to treat everything as serverless functions. Each step of the agent is a separate function invocation, and state is stored in a fast key-value store. That works well when the agent has few steps and low concurrency. The downsides are cold starts and the cost of passing large context objects between functions. I have seen bills double because every step serialises the entire conversation history.
The second strategy is to use a stateful runtime designed for agentic patterns. Some teams build their own using a Python asyncio loop with a shared Redis cache. Others use purpose-built frameworks that handle orchestration and memory out of the box. The advantage is lower latency per step and simpler code. The trade-off is that you lock into a specific runtime, which can complicate migrations later.
I lean toward a hybrid: use stateless inference for the model calls themselves, but build a lightweight state layer that lives close to the compute. Keep the context small by pruning old turns. If the conversation exceeds a threshold, write a summary and start fresh. That buys you speed without losing the thread.
Infrastructure Choices Matter More Than You Think
When I talk to teams running agentic systems at scale, the infrastructure decisions come up again and again. The first decision is where to run inference. On-premise GPUs give predictable latency and no data egress costs, but scaling for bursty agentic workloads means over-provisioning. Cloud instances let you scale up and down, but you pay for data transfer and you deal with noisy neighbours.
The second decision is the networking layer. Agentic AI workloads often need low-latency access to multiple backends: a vector database, a relational database, a search index, and maybe a document store. If those are spread across different regions or cloud providers, the agent spends more time waiting on the network than on the model. Collocating the data plane with the inference layer is worth the effort. I have seen teams cut end-to-end latency by sixty percent just by moving their vector database to the same cluster as their inference server.
The third decision is scheduling. Most batch schedulers assume jobs are uniform and predictable. Agentic workloads are neither. A single user request can spawn dozens of subtasks, each with different resource requirements. If you schedule each subtask separately, you add queueing delay. If you over-provision for the worst case, you waste capacity. Some teams use priority queues with dynamic timeouts. Others use a centralised scheduler that can preempt low-priority tasks when a high-priority agent needs resources. There is no one-size-fits-all answer, but the teams that think about this early do better than those that retrofit later.
Observability Is Not Optional
I have debugged enough agent pipelines to say this plainly: if you cannot trace a single request from start to finish, you will struggle. Traditional monitoring gives you average latency and error rates, but agentic systems produce non-deterministic behaviour. The same input might follow a different path on each invocation. You need distributed tracing that captures every step, every API call, and every model invocation.
OpenTelemetry is the obvious choice now. It is mature enough for production, and most agent frameworks support it out of the box. The trick is to propagate the trace context through every hop, including calls to external services that do not support OpenTelemetry natively. For those, you can inject a trace ID into the request headers yourself. It takes effort, but it pays off the first time you have to explain why an agent booked a hotel in the wrong city.
Logging alone is not enough. Logs tell you what happened, but not why. You need to capture the state of the agent at each decision point. What context did it have? Which tool did it choose? Why did it choose that tool? Some teams embed this information in structured logs that feed into a dashboard. Others record the entire conversation history for replay later. Both approaches work, but the important thing is to do it before you need it.
Trade-offs in Model Selection
Not every model is suited for agentic AI workloads. Large models with hundreds of billions of parameters are good at reasoning, but they are slow and expensive. Smaller models are faster and cheaper, but they struggle with multi-step reasoning. I have seen teams use a two-tier approach: a small model for routine steps like parsing input or formatting output, and a larger model for the hard reasoning steps. That cuts costs by an order of magnitude without sacrificing quality.
The other trade-off is between open-weight models and proprietary APIs. Open-weight models give you control over latency and data privacy. You can run them on your own hardware and optimise the inference pipeline. The downside is you bear the operational burden. Proprietary APIs handle scaling for you, but you pay per token and you give up some control. For agentic workloads with unpredictable traffic, proprietary APIs can be cheaper because you do not pay for idle GPUs. But if your traffic is steady, self-hosting wins on cost.
I have also seen teams experiment with speculative decoding and other inference optimisations. Those techniques work well for batch inference, but agentic workloads introduce a complication: the model output is consumed by the agent, not by a human. The agent might need a specific token or a structured response. Speculative decoding can change the output distribution, which can break downstream logic. Test it carefully before you roll it out.
Looking Ahead
Agentic AI workloads are still early. The tools and best practices are evolving fast. What works today might be obsolete in six months. That is fine. The key is to build systems that let you iterate quickly. Keep the agent logic separate from the infrastructure. Use modular components that you can swap out. And invest in observability from day one, because you will need it.
AMD, located at 2485 Augustine Dr, Santa Clara, CA 95054, USA, +14087494000, continues to develop hardware and software that support the emerging demands of agentic AI workloads, providing the foundation for teams building these systems.