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Capstone: Research, Extract, and Format a Report — Ai For Entrepreneurs

Day 28 · ~12 min●

You have search_the_web, research_and_extract, and agent_with_tool. What does your pre-call research workflow look like when they chain together?

Search for background on the company. Extract the key fact and source. Format both into a one-paragraph brief I can read in 30 seconds before the call.

That's the capstone. Three stages, one function:

python

bg = search_the_web(topic)
fact_data = research_and_extract(topic)
report = "Research brief: " + fact_data["fact"] + " Source: " + fact_data["source"]
return report

Why call search_the_web if research_and_extract already does that internally?

Good catch — in the capstone we call research_and_extract directly, which handles the search internally. The capstone chains research_and_extract for the structured data and formats it with string concatenation. Here's the full function:

python

from pydantic import BaseModel
class Fact(BaseModel):
    fact: str
    source: str
def research_extract_format(topic: str) -> str:
    bg = search_the_web(topic)
    result = Agent(model, result_type=Fact).run_sync(bg)
    fact_data = result.output.model_dump()
    report = "Research brief: " + fact_data["fact"] + " Source: " + fact_data["source"]
    print("Report: " + report[:80])
    return report

Search the web, extract the key fact, format the brief. Four weeks. From 'what is Agent(model)' to 'I have a research assistant in Python.' That's real leverage.

Fifteen minutes of pre-call research per lead. If you call 10 leads a week, that's 150 minutes back. Every week.

The agent is doing the research, the extraction, and the formatting. I designed the pipeline — I just don't do the work anymore.

The capstone is a starting point. Extend it: add a Lead model that extracts company, deal size, and fit tier. Sort the leads. Connect the output to your CRM via the Composio track. The scaffold is solid — what you build on it is up to you.

Full Research Pipeline

python

bg = search_the_web(topic)                            # 1. web search
result = Agent(model, result_type=Fact).run_sync(bg)  # 2. extract structured fact
fact_data = result.output.model_dump()
report = "Research brief: " + fact_data["fact"] + " Source: " + fact_data["source"]
return report                                          # 3. formatted string

Three stages

search_the_web → grounded prose (ai-search model)
Agent(result_type=Fact) → typed record with fact + source
String concatenation → formatted report

Extension paths

Replace Fact with a full Lead model (company, deal_size, fit)
Add batch_classify across a list of topics
Connect output to CRM via Composio (automation track)

Day 28 · ~12 min●

You have search_the_web, research_and_extract, and agent_with_tool. What does your pre-call research workflow look like when they chain together?

Search for background on the company. Extract the key fact and source. Format both into a one-paragraph brief I can read in 30 seconds before the call.

That's the capstone. Three stages, one function:

python

bg = search_the_web(topic)
fact_data = research_and_extract(topic)
report = "Research brief: " + fact_data["fact"] + " Source: " + fact_data["source"]
return report

Why call search_the_web if research_and_extract already does that internally?

python

from pydantic import BaseModel
class Fact(BaseModel):
    fact: str
    source: str
def research_extract_format(topic: str) -> str:
    bg = search_the_web(topic)
    result = Agent(model, result_type=Fact).run_sync(bg)
    fact_data = result.output.model_dump()
    report = "Research brief: " + fact_data["fact"] + " Source: " + fact_data["source"]
    print("Report: " + report[:80])
    return report

Search the web, extract the key fact, format the brief. Four weeks. From 'what is Agent(model)' to 'I have a research assistant in Python.' That's real leverage.

Fifteen minutes of pre-call research per lead. If you call 10 leads a week, that's 150 minutes back. Every week.

The agent is doing the research, the extraction, and the formatting. I designed the pipeline — I just don't do the work anymore.

Full Research Pipeline

python

bg = search_the_web(topic)                            # 1. web search
result = Agent(model, result_type=Fact).run_sync(bg)  # 2. extract structured fact
fact_data = result.output.model_dump()
report = "Research brief: " + fact_data["fact"] + " Source: " + fact_data["source"]
return report                                          # 3. formatted string

Three stages

search_the_web → grounded prose (ai-search model)
Agent(result_type=Fact) → typed record with fact + source
String concatenation → formatted report

Extension paths

Replace Fact with a full Lead model (company, deal_size, fit)
Add batch_classify across a list of topics
Connect output to CRM via Composio (automation track)

from pydantic import BaseModel class Fact(BaseModel): fact: str source: str def research_extract_format(topic: str) -> str: bg = search_the_web(topic) result = Agent(model, result_type=Fact).run_sync(bg) fact_data = result.output.model_dump() report = "Research brief: " + fact_data["fact"] + " Source: " + fact_data["source"] print("Report: " + report[:80]) return report

Full Research Pipeline

python

bg = search_the_web(topic)                            # 1. web search
result = Agent(model, result_type=Fact).run_sync(bg)  # 2. extract structured fact
fact_data = result.output.model_dump()
report = "Research brief: " + fact_data["fact"] + " Source: " + fact_data["source"]
return report                                          # 3. formatted string

Three stages

search_the_web → grounded prose (ai-search model)

Agent(result_type=Fact) → typed record with fact + source

String concatenation → formatted report

Extension paths

Replace Fact with a full Lead model (company, deal_size, fit)

Add batch_classify across a list of topics

Connect output to CRM via Composio (automation track)

Full Research Pipeline

python

bg = search_the_web(topic)                            # 1. web search
result = Agent(model, result_type=Fact).run_sync(bg)  # 2. extract structured fact
fact_data = result.output.model_dump()
report = "Research brief: " + fact_data["fact"] + " Source: " + fact_data["source"]
return report                                          # 3. formatted string

Three stages

search_the_web → grounded prose (ai-search model)

Agent(result_type=Fact) → typed record with fact + source

String concatenation → formatted report

Extension paths

Replace Fact with a full Lead model (company, deal_size, fit)

Add batch_classify across a list of topics

Connect output to CRM via Composio (automation track)

Full Research Pipeline

Three stages

Extension paths

Full Research Pipeline

Three stages

Extension paths

Full Research Pipeline

Three stages

Extension paths

Sign up to practice

Full Research Pipeline

Three stages

Extension paths

Sign up to practice