nimble

Adapt This Agent

Morning Signal is a GTM intelligence example, but the useful part is the agent pattern:

approved context -> live search -> structured extraction -> evidence packet -> grounded synthesis

Use this guide when turning the starter kit into another source-grounded agent.

1. Change The Domain Context

Start with the context a human should approve before retrieval begins.

For GTM intelligence, that context is:

For other domains, replace those fields with the decisions that shape search quality.

Examples:

2. Redesign The Search Lanes

Search lanes are the bridge between approved context and live web evidence.

In lib/agent.ts, adapt planQueries() so every query has:

Good lanes are narrow enough to retrieve useful evidence, but broad enough to surface new signals.

3. Keep Retrieval Separate From Synthesis

Avoid asking the LLM to “research this” directly. Keep the stages separate:

  1. Plan queries.
  2. Search the web.
  3. Extract selected URLs.
  4. Assemble evidence.
  5. Synthesize from the evidence packet.

That separation makes the agent easier to debug, evaluate, and trust.

4. Change The Output Schema

Update lib/types.ts for your new report structure, then update:

Useful output sections usually answer:

5. Preserve Source Inspectability

Every important claim should keep a path back to source evidence.

At minimum, preserve:

Do not add citations as decoration after synthesis. Pass sources through the data model from retrieval to final output.

Example Adaptations

Recruiting Signal Agent

Search lanes:

Output:

Competitor Pricing Monitor

Search lanes:

Output:

Docs Change Watcher

Search lanes:

Output: