Agent Cost and Latency Control
Find where your agent spends money and time, then cut both without losing quality
A taste of a lesson
Our agent takes about 40 seconds per answer and users complain. Where should I look first?
Look at the trace for a typical slow run and count sequential model calls. Forty seconds usually means several steps in a row, each waiting for the last. Then check three things: are any tool calls independent and runnable in parallel, is the agent making lookups it does not need, and is a large model used for simple steps like formatting the final answer? Streaming the final answer will make it feel faster, but fix the steps first. Can you tell me how many model calls happen in one of those 40 second runs?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Measure token use, cost and latency per run and per step
- Find the biggest sources of waste by reading agent traces
- Apply trimming, caching, model routing, parallel calls and step limits
- Pair every optimisation with a quality check so savings do not break results
Lesson plan
- 1 Where agents spend money and time Understand how steps, context growth and model choice drive cost and latency. Start
- 2 Measuring a baseline Collect per step numbers so improvements can be proven. Start
- 3 Smaller contexts Reduce what is resent at every step without removing what the agent needs. Start
- 4 The right model for each step Route routine steps to cheaper models and keep capable ones for hard decisions. Start
- 5 Fewer and faster steps Cut steps and waiting time through parallelism, limits and simpler designs. Start
- 6 Optimising without breaking quality Make cost and speed changes safely with a repeatable evaluation. Start
Try asking
About this tutor
For developers and team leads whose agent works but costs too much or takes too long. Agents spend in ways that are easy to miss: growing context resent at every step, unnecessary tool calls, oversized models for simple steps, and retries. You will learn to measure cost and latency per run and per step, read traces for waste, and apply the main levers: shorter prompts and results, caching where your provider supports it, smaller models for routine steps, parallel tool calls, step limits and early exits. Every change is tested against a quality check so savings do not quietly break the agent.
Reviews
4.0
3 ratingsSample
- Oscar V.Sample
Useful framework, but I wanted more on caching specifics. The tutor stays deliberately general because providers differ, which I understand.
- Mei C.Sample
Cost per successful task instead of cost per run changed our whole conversation with finance. Practical and honest about trade offs.
- Leon H.Sample
Measuring first sounded obvious, but our biggest cost turned out to be retries on one flaky tool, not the model. Good, grounded lessons.
About the teacher
I teach how AI agents are built: the loop, the tools, the memory, and when a plain workflow is the better choice
9 tutors 310 lessons taught Sample
I build and teach the inner workings of AI agents. Most of my working life has been spent on backend systems, so I approach agents the way I approach any distributed system: what runs, in what order, what can fail, and what it costs. I like to start every topic with a drawing of the loop on a whiteboard and...
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