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How to decide between a fixed pipeline and an agent using cost and latency data with AI Engineer Notebooks

Compare a fixed pipeline with an agent loop on cost, latency, and quality before you pick one.

Authorization notice. AI Engineer Notebooks is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Who it is forA solutions engineer or architect who has to recommend an architecture for a customer task and must justify the cost.

The problemTeams often build an agent when a fixed pipeline would do, which adds calls, latency, and cost without a quality gain.

The outcomeA side-by-side comparison of a pipeline and an agent on the same task, with a reasoned recommendation.

What you need first

  • A Google account to open Colab notebooks
  • A free Groq API key from console.groq.com (no credit card needed)
  • The key saved as a Colab secret, as the setup notebook describes
  • Basic Python and comfort reading API responses

Steps

  1. Run 05-agents/01-agent-loop-from-scratch.ipynb to build a working agent loop with raw API calls.
  2. Build the same task as a fixed pipeline using the RAG and tool calling patterns from sections 01 and 03.
  3. Open 05-agents/03-guardrails-and-budgets.ipynb and read the section on when a pipeline beats an agent.
  4. Measure cost, latency, and answer quality for both versions on the same golden set.
  5. Recommend the cheaper option that meets the quality bar and record the numbers.

Prompt for your agent

Using the repo calmrocks/ai-engineer-notebooks, build the agent loop from 05-agents/01-agent-loop-from-scratch.ipynb and a fixed pipeline for <placeholder task>. Expected output: a table comparing calls per task, latency, and quality for each. Verify that both versions were scored on the same set of inputs.

Caveat. The README names the pipeline-vs-agent cost comparison as a case study but does not give its results, so your numbers will come from your own runs.

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