Choose the smallest building block that works — decision exercise
Exercise: choose the smallest building block that works
Original fictional scenario. No cloud account, API calls or paid services are required. Difficulty: intermediate · Estimated duration: 15–20 minutes
Harbor Mutual, an invented insurer, has a small AI team (two developers, no search specialists) and four requests this quarter.
| Request | Detail |
|---|---|
| A | Compare three open models for summarizing claim photos' captions and deploy the best. Security wants only vetted models deployable. |
| B | A chatbot for brokers that answers from 3,000 policy PDFs, live within six weeks. |
| C | Predict which claims will need an adjuster visit, from a historic claims table. Nobody on the team writes training code. |
| D | An assistant that checks a claim's status, books an adjuster and emails the policyholder. |
Your decision
- Name the primary building block for each request and the reason.
- For B, decide between prebuilt RAG and assembling RAG from component APIs. What would have to be true to change your answer?
- For D, name two operating controls you would require before go-live, and what each guarantees.
- Which request, if any, needs a custom agent, and why do the others not?
Rubric (10 house points)
- 4 points: one correct block per request with its reason.
- 2 points: prebuilt RAG for B, and a valid trigger for the APIs (a named need for granular control).
- 2 points: two correct controls for D with their purpose.
- 2 points: only D needs a custom agent, because only D must take actions through tools.
Reference solution
A → Model Garden, the library to discover, test, customize and deploy models, governed by a Model Garden organization policy that limits access to vetted models. B → prebuilt RAG with Agent Search, an out-of-the-box RAG system reduced to a few clicks; a team without search specialists should not build RAG itself, which Google warns can be complex. C → AutoML, which prototypes models with minimal technical effort. D → a custom agent built with ADK, whose tools integrate Harbor's systems, run on Agent Runtime so the team does not manage infrastructure.
For B, switch to the component APIs only if a requirement names a part of the pipeline the team must control — for example a custom ranking rule demanded by compliance.
Controls for D: Agent Gateway to govern every tool call and its authentication (for example, booking allowed, payments not), and Agent Identity so each action is attributable for audit.
Rejected alternatives: a custom agent for B (the task only answers questions); custom training for C (the team cannot write a training application); Agent Search for A (it retrieves information; it is not a model library).
Sources and scope
All organisations, names, numbers, thresholds, the rubric and the conclusions above are house-authored. The product facts the reasoning depends on are grounded in:
- https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/model-garden/explore-models
- https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/start/training-methods
- https://cloud.google.com/products/gemini-enterprise-agent-platform/agent-search
- https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/adk
- https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/runtime
- https://docs.cloud.google.com/gemini-enterprise-agent-platform/overview