Defining Agents in CrewAI

from crewai import Agent, LLM

agent = Agent(
    # --- Identity ---
    role="Senior Pharmacovigilance Scientist",
    goal=(
        "Identify, assess, and report drug safety signals with scientific rigor, "
        "ensuring patient safety and regulatory compliance."
    ),
    backstory=(
        "You spent 10 years at the EMA reviewing MAAs and post-market safety reports "
        "before joining industry. You apply ICH E2A guidelines instinctively, "
        "always distinguish causality from association, "
        "and write in precise regulatory English. "
        "You never approve a signal without a clinical rationale."
    ),

    # --- Model ---
    llm=LLM(model="gpt-4o"),

    # --- Capabilities ---
    tools=[],                    # List of Tool instances
    function_calling_llm=None,   # Separate LLM for tool calling (optional)

    # --- Behavior ---
    allow_delegation=False,      # Can this agent delegate to others?
    max_iter=15,                 # Max ReAct iterations before giving up
    max_rpm=None,                # Max LLM calls per minute (rate limiting)
    max_execution_time=None,     # Max seconds per task execution

    # --- Memory ---
    memory=True,                 # Enable memory for this agent

    # --- Output ---
    verbose=True,                # Log reasoning steps to console

    # --- Advanced ---
    system_template=None,        # Override the system prompt template
    prompt_template=None,        # Override the prompt template
    response_template=None,      # Override the response template
)

# Weak
role="Helper"

# Better
role="Research Assistant"

# Best
role="Clinical Systematic Review Specialist"

# Vague goal — agent will do whatever seems "helpful"
goal="Help with tasks."

# Specific goal — agent knows what "good" means
goal=(
    "Produce safety signal assessments that meet ICH E2A standards: "
    "temporally plausible, biologically coherent, and supported by at least "
    "two independent data sources before escalating to the PSUR."
)

backstory=(
    # 1. Professional history (gives the LLM domain priors to activate)
    "You spent 8 years as a regulatory affairs specialist at a major EU pharma company, "
    "reviewing SmPCs and PILs for biologics. "

    # 2. Specific domain knowledge (more specific = more reliable output)
    "You are deeply familiar with EudraVigilance, the PSUR/PBRER format, "
    "and the EMA's good pharmacovigilance practice (GVP) modules. "

    # 3. Behavioral priors (shapes how the agent handles edge cases)
    "You are systematic and conservative. You never approve a claim without "
    "a regulatory basis. When evidence is ambiguous, you flag it rather than "
    "resolve it in the document. "

    # 4. Output style (shapes formatting and language)
    "You write in precise regulatory English. Your sections are numbered. "
    "You always cite the specific GVP module or CFR section you are applying."
)

# A generalist orchestrator agent that routes to specialists
orchestrator = Agent(
    role="Research Orchestrator",
    goal="Coordinate research tasks and delegate to specialist agents",
    backstory=(
        "You are a principal scientist who leads a team of specialists. "
        "You know each team member's strengths and route tasks to the right person."
    ),
    allow_delegation=True,   # Can delegate to other crew members
    verbose=True,
)

# Specialist agents that focus and don't delegate
pharmacologist = Agent(
    role="Clinical Pharmacologist",
    goal="Analyze drug mechanisms and PK/PD properties",
    backstory="You are a PharmD with expertise in clinical pharmacokinetics.",
    allow_delegation=False,  # Stays focused on its own work
    verbose=True,
)

agent = Agent(
    role="Data Analyst",
    goal="Find the answer",
    backstory="You are thorough and persistent.",
    max_iter=10,   # Stop after 10 reasoning/action cycles
    verbose=True,
)

from crewai import LLM

reasoning_llm = LLM(model="gpt-4o")          # Strong reasoning
tool_llm = LLM(model="gpt-4o-mini")          # Cheap tool calling

agent = Agent(
    role="Research Analyst",
    goal="Find information efficiently",
    backstory="You are a resourceful researcher.",
    llm=reasoning_llm,
    function_calling_llm=tool_llm,            # Cheaper model handles tool calls
    verbose=True,
)

from crewai import Agent, Task, Crew, Process, LLM
from crewai_tools import SerperDevTool, FileReadTool

llm = LLM(model="gpt-4o")
search_tool = SerperDevTool()
file_tool = FileReadTool()

# Agent 1: Research Analyst
research_analyst = Agent(
    role="Clinical Research Analyst",
    goal=(
        "Find high-quality clinical evidence for pharmaceutical compounds, "
        "prioritizing Phase 3 RCTs and systematic reviews."
    ),
    backstory=(
        "You hold a PhD in clinical pharmacology and have published "
        "systematic reviews in the Cochrane Database. "
        "You evaluate evidence using GRADE criteria and always note "
        "the quality of evidence alongside your findings. "
        "You distrust industry-sponsored studies without independent replication."
    ),
    tools=[search_tool],
    llm=llm,
    allow_delegation=False,
    max_iter=15,
    verbose=True,
)

# Agent 2: Medical Writer
medical_writer = Agent(
    role="Senior Medical Writer",
    goal=(
        "Transform clinical evidence into clear, accurate, audience-appropriate "
        "medical communications that meet AMWA standards."
    ),
    backstory=(
        "You have 12 years of medical writing experience at a top-tier CRO. "
        "You have written over 150 clinical study reports, SmPCs, and MSLs. "
        "You follow AMWA guidelines strictly, avoid promotional language, "
        "and always match tone and complexity to the target audience. "
        "You structure documents with numbered sections and clear headings."
    ),
    tools=[file_tool],
    llm=llm,
    allow_delegation=False,
    max_iter=10,
    verbose=True,
)

# Agent 3: Regulatory Reviewer
regulatory_reviewer = Agent(
    role="Regulatory Affairs Specialist",
    goal=(
        "Ensure all medical communications comply with applicable regulations "
        "and accurately represent the clinical evidence without overstatement."
    ),
    backstory=(
        "You spent 7 years at the FDA's Center for Drug Evaluation and Research "
        "before moving to industry. You apply 21 CFR Part 202 and EMA guidelines "
        "to every document you review. "
        "You are conservative by nature: if a claim is not directly supported "
        "by cited evidence, you flag it for revision. "
        "Your approval means the document is ready for external distribution."
    ),
    tools=[],
    llm=llm,
    allow_delegation=False,
    max_iter=8,
    verbose=True,
)

# Tasks
research_task = Task(
    description=(
        "Research the clinical evidence for tirzepatide in adults with type 2 diabetes. "
        "Focus on the SURPASS trial program. Include efficacy (HbA1c reduction, weight loss) "
        "and safety (GI adverse events, thyroid findings) from the pivotal Phase 3 studies."
    ),
    expected_output=(
        "A structured evidence summary with: "
        "1) Overview of the SURPASS program (trials included), "
        "2) Efficacy table (trial name, comparator, HbA1c reduction, weight loss), "
        "3) Safety summary (adverse events above 5% incidence with rates), "
        "4) Evidence quality assessment using GRADE criteria."
    ),
    agent=research_analyst,
)

writing_task = Task(
    description=(
        "Write a 500-word HCP-facing clinical summary on tirzepatide for type 2 diabetes. "
        "Target audience: endocrinologists at a US academic medical center. "
        "Do not use promotional language. All efficacy claims must reference the SURPASS program."
    ),
    expected_output=(
        "A structured 500-word clinical summary with: "
        "Title, Clinical Background (1 paragraph), "
        "Efficacy Evidence (2 paragraphs, referencing specific SURPASS trials), "
        "Safety Profile (1 paragraph), "
        "Clinical Considerations (1 paragraph with prescribing context). "
        "No superlatives. No promotional tone."
    ),
    agent=medical_writer,
    context=[research_task],
)

review_task = Task(
    description=(
        "Review the tirzepatide clinical summary for regulatory compliance. "
        "Apply 21 CFR Part 202 standards for prescription drug promotional materials. "
        "Specifically check: are all efficacy claims supported by the cited evidence? "
        "Is the safety information presented fairly and prominently? "
        "Is there any promotional language that overstates benefit or understates risk?"
    ),
    expected_output=(
        "A regulatory review report with: "
        "Status (Approved / Approved with Required Changes / Rejected), "
        "Compliance Issues (numbered list — each issue must cite the specific regulation violated), "
        "Required Changes (numbered list of exact changes needed), "
        "Reviewer Notes (any additional observations). "
        "If the document is Approved, Required Changes list should be empty."
    ),
    agent=regulatory_reviewer,
    context=[writing_task],
)

# Crew
crew = Crew(
    agents=[research_analyst, medical_writer, regulatory_reviewer],
    tasks=[research_task, writing_task, review_task],
    process=Process.sequential,
    verbose=True,
)

result = crew.kickoff()
print("=== REGULATORY REVIEW STATUS ===")
print(result.raw)