AI Agents Explained

You are a helpful research assistant with access to the following tools:
{tools}

To answer the user's question, use this exact format:

Thought: I need to figure out [what you're thinking]
Action: [tool_name]
Action Input: [input for the tool]
Observation: [result from the tool — you'll receive this]

...repeat Thought/Action/Observation as needed...

Thought: I now have enough information to answer.
Final Answer: [your complete answer to the user]

Important rules:
- Always start with a Thought.
- Never make up tool results — wait for the Observation.
- If a tool fails, explain why and try a different approach.
- Limit yourself to {max_iterations} iterations.

User question: {question}

# Define a tool for your agent
tool_name: "search_company_database"
description: "Search the internal company database for customer records, orders, or product information. Use this when the user asks about specific customers, orders, or products."
parameters:
  query:
    type: string
    description: "The search query — can be a customer name, order ID, or product name"
    required: true
  limit:
    type: integer
    description: "Maximum number of results to return (default: 5)"
    required: false
returns: "A JSON array of matching records with id, name, and relevant fields"
error_handling: "If no results found, return an empty array and suggest the user refine their query"

# Research Agent (Pseudocode — works with any framework)

GOAL = "Research {topic} and produce a 500-word summary with citations"

TOOLS = [
    web_search(query) → list of URLs,
    extract_content(url) → text content,
    summarize(texts, format) → structured summary,
]

GUARDRAILS = {
    max_iterations: 3,
    max_tool_calls: 10,
    timeout_per_call: 60s,
    fallback: "Could not find reliable sources for this topic."
}

# The Agent Loop (PRPAL)
for iteration in range(max_iterations):
    
    # 1. PERCEIVE: What do I know so far?
    context = get_current_context()
    
    # 2. REASON: What should I do next?
    next_action = llm.reason(GOAL, context, TOOLS)
    
    # 3. PLAN: Break it down
    if iteration == 0:
        plan = llm.create_plan(GOAL, TOOLS)
    
    # 4. ACT: Execute the next step
    result = execute_tool(next_action.tool, next_action.input)
    
    # 5. LEARN: Did it work? Am I done?
    evaluation = llm.evaluate(result, GOAL)
    
    if evaluation.is_complete:
        return evaluation.final_answer
    
    # Not done — update context and loop
    update_context(result)

return GUARDRAILS.fallback

# Agent Identity (Role-Goal-Backstory Framework)

role: "Senior UX Researcher specializing in user interview analysis"

goal: "Uncover actionable user insights by analyzing interview data
and identifying recurring patterns, unmet needs, and improvement
opportunities that the product team can act on immediately"

backstory: "You have spent 15 years conducting and analyzing user
research for top tech companies. You have a talent for reading
between the lines and identifying patterns that others miss.
You believe that good UX is invisible and that the best insights
come from listening to what users don't say as much as what they
do say."

# Key principle: Specialists > Generalists
# "Writer" → weak. "Technical Blog Writer specializing in
# explaining complex AI concepts to non-technical audiences" → strong.