How to Build a Multi-Agent Policy Briefing Pipeline in Python¶

A ministerial brief pulls together economic, legal, and social analysis — work that naturally maps to departments and the analysts within them. This recipe uses the Hierarchical pattern: a director decomposes the question into department subtasks, each department lead coordinates its analysts, and results flow back up into one synthesized brief.

Patterns used: Hierarchical

Architecture¶

flowchart TD
    Q[Policy Question] --> D[Director\nDecompose + synthesize]
    D --> EL[Economics Lead]
    D --> LL[Legal Lead]
    EL --> E1[Fiscal Analyst]
    EL --> E2[Labor Analyst]
    LL --> L1[Statute Analyst]
    LL --> L2[Rights Analyst]
    E1 --> D
    E2 --> D
    L1 --> D
    L2 --> D
    D --> B[Ministerial Brief]

Implementation¶

import asyncio
from pyagent_patterns.base import Agent
from pyagent_patterns.orchestration import Hierarchical
from pyagent_patterns.orchestration.hierarchical import Team
from pyagent_providers import AnthropicLLM, OpenAILLM

briefing = Hierarchical(
    manager=Agent(
        "policy_director",
        AnthropicLLM("claude-sonnet-4-20250514"),
        system_prompt=(
            "Decompose the policy question into subtasks for the Economics and Legal teams. "
            "After receiving both teams' outputs, synthesize a 1-page ministerial brief: "
            "summary, options, risks, and a recommendation. Flag where teams disagree."
        ),
    ),
    teams=[
        Team(
            name="Economics",
            lead=Agent(
                "economics_lead",
                OpenAILLM("gpt-4o-mini"),
                system_prompt="Coordinate the fiscal and labor analyses into one economic assessment.",
            ),
            workers=[
                Agent(
                    "fiscal_analyst",
                    OpenAILLM("gpt-4o-mini"),
                    system_prompt="Estimate budget impact, revenue effects, and cost over 5 years.",
                ),
                Agent(
                    "labour_analyst",
                    OpenAILLM("gpt-4o-mini"),
                    system_prompt="Assess employment, wage, and regional labor-market effects.",
                ),
            ],
        ),
        Team(
            name="Legal",
            lead=Agent(
                "legal_lead",
                OpenAILLM("gpt-4o-mini"),
                system_prompt="Coordinate statutory and rights analyses into one legal assessment.",
            ),
            workers=[
                Agent(
                    "statute_analyst",
                    OpenAILLM("gpt-4o-mini"),
                    system_prompt="Identify enabling legislation, required amendments, and precedent.",
                ),
                Agent(
                    "rights_analyst",
                    OpenAILLM("gpt-4o-mini"),
                    system_prompt="Assess civil-rights, privacy, and equality-impact considerations.",
                ),
            ],
        ),
    ],
)

result = asyncio.run(briefing.run(
    "Should the city introduce a congestion charge for the downtown core?"
))
print(result.output)
print(f"Teams: {result.metadata['team_names']}, workers: {result.metadata['total_workers']}")

Expected output¶

MINISTERIAL BRIEF — Downtown Congestion Charge

Summary:   A £12/day charge could cut peak traffic ~18% and raise ~£40M/yr.
Economics: net fiscal positive by year 2; labor impact concentrated in logistics.
Legal:     requires a Transport Act amendment; equality assessment needed for low-income drivers.
Options:   (A) full charge, (B) peak-only, (C) phased pilot.
Recommendation: Option C — 12-month pilot with exemptions, then review.

Teams: ['Economics', 'Legal'], workers: 4

Customization¶

Add a costing worker¶

briefing.teams[0].workers.append(
    Agent("costing_analyst", OpenAILLM("gpt-4o-mini"),
          system_prompt="Produce a 5-year cost-benefit estimate with assumptions."),
)

Add a third team¶

from pyagent_patterns.orchestration.hierarchical import Team
briefing.teams.append(
    Team(name="Public Health",
         lead=Agent("ph_lead", OpenAILLM("gpt-4o-mini"), system_prompt="Coordinate the public-health assessment."),
         workers=[Agent("epi", OpenAILLM("gpt-4o-mini"), system_prompt="Assess health and equity impacts.")]),
)

Cheaper drafts¶

Swap the director to claude-haiku-3-5-20241022 for internal drafts; reserve sonnet for ministerial briefs.

When to Use¶

Situation	Use Hierarchical?
Work decomposes into departments/teams with their own sub-work	✅ Yes
You need one synthesized output from many specialists	✅ Yes
There's a single flat pool of workers, no team structure	❌ Use Orchestrator-Workers
Specialists should debate to a verdict	❌ Use Debate

Cost Profile¶

Tier	Typical model	Avg cost	Volume (100 briefs/mo)
Director (decompose + synthesize)	claude-sonnet	$0.008	$0.80/mo… ×100 = $80
Leads ×2 + analysts ×4	gpt-4o-mini	$0.004	$40/mo
Per brief	mix	~$0.012	~$120/mo