%%capture
%pip install -q anthropic python-dotenv

import os
 
import anthropic
from dotenv import load_dotenv
 
load_dotenv()
 
BETAS = ["managed-agents-2026-04-01"]
MODEL = os.environ.get("COOKBOOK_MODEL", "claude-opus-4-6")
client = anthropic.Anthropic()

def make_agent(name, description, system, tools):
    a = client.beta.agents.create(
        name=name,
        description=description,
        model=MODEL,
        system=system,
        tools=tools,
        betas=BETAS,
    )
    print(f"{name}: {a.id}")
    return a.id
 
 
prospect_researcher = make_agent(
    "prospect_researcher",
    "Researches what companies in a given industry segment and size tier typically prioritize.",
    """Given a prospect's industry and size, use web search to find:
- What companies in that segment typically list as strategic priorities
- Recent trends or pressures in that industry
- Common operational pain points at that scale
Return via send_to_parent: {"priorities": [...], "recent_moves": [...], "pain_points": [...], "sources": [...]}""",
    [
        {
            "type": "agent_toolset_20260401",
            "configs": [{"name": "web_search"}, {"name": "web_fetch"}],
        }
    ],
)
 
case_study_picker = make_agent(
    "case_study_picker",
    "Selects the two most relevant case studies from the library for a given prospect profile.",
    """The case study library is in /mnt/user-data/case_studies/. Each file is one customer story.
You will be given a prospect's industry, size, and top priorities. Read the library, score each study on relevance, and pick the two best matches.
Return via send_to_parent: {"picks": [{"file": ..., "customer": ..., "why_relevant": ...}, ...]}""",
    [{"type": "agent_toolset_20260401"}],
)
 
pricing_modeler = make_agent(
    "pricing_modeler",
    "Builds two or three pricing options for a prospect based on seat count and expected usage.",
    """Pricing rules are in /mnt/user-data/pricing_rules.md. Given a prospect's estimated seat count and usage tier, build:
- a conservative option (annual commit, lower per-seat)
- a flexible option (monthly, higher per-seat)
- if seat count > 500, an enterprise option with a platform fee
Show the first-year total for each. Return via send_to_parent: {"options": [{"name": ..., "structure": ..., "year_one_total": ...}, ...]}""",
    [{"type": "agent_toolset_20260401"}],
)

CASE_STUDIES = [
    {
        "slug": "stclair_health",
        "title": "St. Clair Health",
        "industry": "regional hospital network",
        "employees": 6200,
        "summary": """Challenge: credentialing and prior-auth workflows spread across 11 systems.
Result with Northstar: consolidated to 3 automated workflows; prior-auth turnaround down 58%; $1.9M annual labor savings.""",
    },
    {
        "slug": "blueridge_health_plan",
        "title": "BlueRidge Health Plan",
        "industry": "regional payer",
        "employees": 2800,
        "summary": """Challenge: claims-adjudication exceptions queued in email; 19% required manual rework.
Result with Northstar: exception routing automated end-to-end; rework rate down to 6%; 11-day faster average claim resolution.""",
    },
    {
        "slug": "calder_mfg",
        "title": "Calder Manufacturing",
        "industry": "industrial",
        "employees": 3100,
        "summary": """Challenge: purchase-order approvals averaging 9 days.
Result with Northstar: PO cycle time cut to 2.1 days; 14% reduction in maverick spend.""",
    },
    {
        "slug": "northwind",
        "title": "Northwind Logistics",
        "industry": "3PL",
        "employees": 4400,
        "summary": """Challenge: carrier-onboarding paperwork took 3 weeks per carrier.
Result with Northstar: onboarding down to 4 days; 22% more carriers activated in Q1.""",
    },
    {
        "slug": "harborview_retail",
        "title": "Harborview Retail Group",
        "industry": "specialty retail",
        "employees": 5600,
        "summary": """Challenge: store-level inventory exceptions handled by regional managers over Slack and spreadsheets.
Result with Northstar: exception triage automated across 140 stores; stockout incidents down 31%.""",
    },
    {
        "slug": "aperture_fintech",
        "title": "Aperture Payments",
        "industry": "fintech",
        "employees": 1900,
        "summary": """Challenge: KYC and merchant-onboarding reviews averaging 6 business days.
Result with Northstar: review SLA cut to 36 hours; onboarding throughput up 2.4x with the same team.""",
    },
    {
        "slug": "summit_county",
        "title": "Summit County Government",
        "industry": "public sector",
        "employees": 3700,
        "summary": """Challenge: building-permit applications routed through five departments by paper packet.
Result with Northstar: single digital intake with parallel department review; median permit time 41 to 17 days.""",
    },
]

PRODUCT = """# Northstar Platform — One-Pager
Northstar is a workflow automation platform for mid-market operations teams.
Core capabilities: visual process builder, 200+ SaaS connectors, role-based approvals, SOC 2 Type II.
Typical results: 40-60% reduction in manual ticket handling, 3-week time-to-first-workflow."""
 
PRICING = """# Pricing Rules (internal)
- Per-seat list: $65/mo (monthly) or $52/mo (annual commit).
- Usage tiers: light = 1.0x, standard = 1.15x, heavy = 1.30x multiplier on per-seat.
- Enterprise (>500 seats): add $48,000/yr platform fee, per-seat drops to $44/mo annual.
- All options include onboarding; enterprise includes a named CSM."""

env = client.beta.environments.create(
    name="proposal-meridian",
    config={"type": "anthropic_cloud", "networking": {"type": "unrestricted"}},
)
 
resources = []
 
 
def mount(path, content):
    f = client.beta.files.upload(
        file=(os.path.basename(path), content.encode(), "text/plain")
    )
    resources.append({"type": "file", "file_id": f.id, "mount_path": path})
 
 
for cs in CASE_STUDIES:
    body = f"# {cs['title']} ({cs['industry']}, {cs['employees']:,} employees)\n{cs['summary']}"
    mount(f"/mnt/user-data/case_studies/{cs['slug']}.md", body)
mount("/mnt/user-data/product_one_pager.md", PRODUCT)
mount("/mnt/user-data/pricing_rules.md", PRICING)
 
coordinator = client.beta.agents.create(
    name="Proposal Writer",
    model=MODEL,
    system="""You assemble tailored sales proposals.
Given a prospect name and basic profile:
1. Send the prospect's industry and size to prospect_researcher.
2. Send the prospect's industry, size, and (once the researcher reports back) their priorities to case_study_picker.
3. Send the seat count and usage tier to pricing_modeler.
4. Read /mnt/user-data/product_one_pager.md, then write /mnt/session/outputs/proposal.md with sections:
   Executive summary (tied to their priorities), How we help (from the one-pager),
   Proof (the two case studies), Investment (the pricing options), Next steps.
Keep it to two pages.""",
    tools=[{"type": "agent_toolset_20260401"}],
    multiagent={
        "type": "coordinator",
        "agents": [prospect_researcher, case_study_picker, pricing_modeler],
    },
    betas=BETAS,
)
 
session = client.beta.sessions.create(
    agent={"type": "agent", "id": coordinator.id, "version": coordinator.version},
    environment_id=env.id,
    resources=resources,
    title="Proposal: Meridian Health",
    betas=BETAS,
)
print(f"Session {session.id} ready with {len(resources)} files mounted")

PROSPECT = {
    "name": "Meridian Health",
    "industry": "regional healthcare system",
    "employees": 8500,
    "estimated_seats": 600,
    "usage_tier": "heavy",
}
 
client.beta.sessions.events.send(
    session.id,
    betas=BETAS,
    events=[
        {
            "type": "user.message",
            "content": [
                {
                    "type": "text",
                    "text": f"Build a proposal for {PROSPECT['name']}, a {PROSPECT['industry']} with "
                    f"~{PROSPECT['employees']} employees. Estimate {PROSPECT['estimated_seats']} seats "
                    f"at {PROSPECT['usage_tier']} usage. Write to /mnt/session/outputs/proposal.md.",
                }
            ],
        }
    ],
)
 
with client.beta.sessions.events.stream(session.id, betas=BETAS) as stream:
    for ev in stream:
        if ev.type == "session.thread_created":
            print(f"[spawn] {ev.agent_name}")
        elif ev.type == "agent.thread_message_received":
            print(f"[report] {ev.from_agent_name} returned")
        elif ev.type == "session.status_idle":
            print("[done]")
            break

def text_of(content):
    return "".join(b.text for b in content if b.type == "text")
 
 
for ev in client.beta.sessions.events.list(session.id, limit=1000, betas=BETAS):
    if ev.type == "agent.thread_message_received":
        body = text_of(ev.content)
        print(f"━━━ send_to_parent from {ev.from_agent_name} ({len(body)} chars) ━━━")
        print(body[:1200] + (f"\n…[{len(body) - 1200} more chars]" if len(body) > 1200 else ""))
        print()

proposal = ""
for ev in client.beta.sessions.events.list(session.id, limit=1000, betas=BETAS):
    if (
        ev.type == "agent.tool_use"
        and ev.name == "write"
        and ev.input["file_path"].endswith("proposal.md")
    ):
        proposal = ev.input["content"]
        break
 
# Show the section structure rather than the full proposal.
for line in proposal.splitlines():
    if line.startswith("#"):
        print(line)