Multi-Agent Systems — Agents Working Together

import anthropic, json
from dataclasses import dataclass, field
from typing import Optional

client = anthropic.Anthropic()

# ── Base agent (same loop from Days 1-3) ──────────
def simple_call(system: str, prompt: str, model="claude-sonnet-4-5") -> str: """Single-turn call to Claude with a system prompt.""" resp = client.messages.create( model=model, max_tokens=2048, system=system, messages=[{"role":"user","content":prompt}] ) return resp.content[0].text

# ── Specialized agents ─────────────────────────────
def planner_agent(task: str) -> list[str]: """Decomposes a research task into 3-5 specific sub-questions.""" print("[Planner] Decomposing task...") system = """You are a research planner. Break down research tasks into 3-5 specific, focused sub-questions that together answer the main question. Return a JSON array of strings. Only return the JSON, no other text.""" result = simple_call(system, f"Task: {task}") try: return json.loads(result) except: # Fallback: extract lines if JSON parsing fails return [line.strip() for line in result.split("\n") if line.strip()][:5]

def researcher_agent(question: str) -> str: """Answers a specific research question with detail and examples.""" print(f"[Researcher] Investigating: {question[:60]}...") system = """You are a thorough research analyst. Answer questions with: - Specific facts and figures when available - Concrete examples to illustrate points - Honest assessment of what you know vs what's uncertain Keep responses focused and evidence-based, 150-250 words.""" return simple_call(system, question)

def writer_agent(task: str, research: dict) -> str: """Synthesizes research into a coherent final report.""" print("[Writer] Synthesizing final report...") research_text = "\n\n".join( f"Q: {q}\nA: {a}" for q, a in research.items() ) system = """You are a clear, engaging writer who synthesizes research into readable reports. Structure: Executive summary (2-3 sentences) → Key findings (bullet points) → Implications → Conclusion. Write for a knowledgeable but non-specialist audience.""" prompt = f"""Original task: {task}

Research findings:
{research_text}

Write the final report.""" return simple_call(system, prompt)

# ── Orchestrator: runs the full pipeline ──────────
def research_pipeline(task: str) -> str: print(f"\n=== Research Pipeline ===\nTask: {task}\n") # Step 1: Planner breaks task into sub-questions questions = planner_agent(task) print(f"Sub-questions: {questions}\n") # Step 2: Researcher answers each sub-question research = {} for q in questions: research[q] = researcher_agent(q) # Step 3: Writer synthesizes everything report = writer_agent(task, research) return report

# ── Debate pattern: two agents argue, then synthesize
def debate(question: str) -> str: print(f"\n=== Debate Pattern ===\nQuestion: {question}\n") # Agent A argues FOR print("[Agent A] Arguing FOR...") for_arg = simple_call( "You must argue FOR the proposition. Be specific, cite evidence, be persuasive.", f"Argue for: {question}" ) # Agent B argues AGAINST (sees Agent A's argument) print("[Agent B] Arguing AGAINST...") against_arg = simple_call( "You must argue AGAINST the proposition. Critique the FOR argument specifically.", f"Proposition: {question}\n\nFOR argument:\n{for_arg}\n\nNow argue against." ) # Synthesizer finds the nuanced truth print("[Synthesizer] Finding nuanced answer...") synthesis = simple_call( "You are a fair judge. Given two opposing arguments, find the nuanced truth. " "Acknowledge what's right in each side. Give a balanced, honest conclusion.", f"""Question: {question}

FOR:
{for_arg}

AGAINST:
{against_arg}

What is the balanced truth?""" ) return synthesis

# ── Run both patterns ─────────────────────────────
if __name__ == "__main__": # Research pipeline report = research_pipeline( "What are the main challenges companies face when adopting AI in 2024?" ) print("\n=== Final Report ===\n", report) # Debate pattern answer = debate("Should companies require AI literacy for all employees?") print("\n=== Debate Synthesis ===\n", answer)