Design · PM · Sales · CS · Marketing

from pathlib import Path
import sys

# Resolve the repo root whether this cell is running from ./notebooks
# or from the repo root — mirrors the existing 01–36 notebook series.
ROOT = (
    Path.cwd().resolve().parent
    if Path.cwd().name == "notebooks"
    else Path.cwd().resolve()
)
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

from examples.run_multi_tool_agent import build_llm_from_env

# Bedrock Llama 4 Scout is the default model this library ships with
# (see `shipit_agent/config.py`). No model name required — the helper
# reads `AWS_REGION` / credentials from env and returns an adapter that
# works with every Autopilot / Agent class.
llm = build_llm_from_env("bedrock")
print("Bedrock LLM ready:", type(llm).__name__)

from shipit_agent import Agent
from shipit_agent.agents import AgentRegistry
from shipit_agent.builtins import get_builtin_tools

registry = AgentRegistry()

design_def = registry.get("design-reviewer")
designer = Agent(
    llm=llm,
    prompt=design_def.prompt,
    tools=get_builtin_tools(project_root="."),
    max_iterations=design_def.maxIterations or 20,
    name=design_def.name,
)
result = designer.run(
    "Review the onboarding flow described in this repo's README and list P0/P1/P2 findings with the principle each violates."
)
print(result.output[:900])

pm_def = registry.get("product-manager")
pm = Agent(
    llm=llm,
    prompt=pm_def.prompt,
    tools=[],
    name=pm_def.name,
    max_iterations=pm_def.maxIterations or 20,
)
result = pm.run(
    "Spec out a lightweight 'share run' feature for Autopilot that lets engineers email a read-only link to a completed run."
)
print(result.output[:900])

sales_def = registry.get("sales-outreach")
sales = Agent(
    llm=llm,
    prompt=sales_def.prompt,
    tools=[],
    name=sales_def.name,
    max_iterations=sales_def.maxIterations or 15,
)
result = sales.run(
    "Prep an outreach for the VP of Engineering at a Series B fintech that just announced a hiring freeze. Our pitch is unattended overnight refactoring runs that cut eng costs."
)
print(result.output[:900])

import os

if os.environ.get("HUBSPOT_TOKEN"):
    from shipit_agent.tools.hubspot import HubspotTool

    sales_with_crm = Agent(
        llm=llm,
        prompt=sales_def.prompt,
        tools=[HubspotTool()],
        max_iterations=12,
        name=sales_def.name,
    )
    print(
        "Sales agent ready with HubSpot tool — replace the .run() call with your actual prospect context."
    )
else:
    print(
        "Set HUBSPOT_TOKEN in your env to enable live CRM lookups — this cell is a no-op without it."
    )

cs_def = registry.get("customer-success")
cs = Agent(
    llm=llm,
    prompt=cs_def.prompt,
    tools=[],
    name=cs_def.name,
    max_iterations=cs_def.maxIterations or 15,
)
result = cs.run(
    "A new $150k/yr customer just signed. Their stated goal was 'cut incident response time by 40%'. Draft the first 30-day success plan and kickoff agenda."
)
print(result.output[:900])

mkt_def = registry.get("marketing-writer")
mkt = Agent(
    llm=llm,
    prompt=mkt_def.prompt,
    tools=[],
    name=mkt_def.name,
    max_iterations=mkt_def.maxIterations or 15,
)
result = mkt.run(
    "Write the launch post for shipit_agent 1.1 — the Autopilot release. Readers are senior engineers. Keep it ≤250 words, concrete, no 'blazing fast'."
)
print(result.output[:900])