# Example 13: Variables & Context
#
# This example demonstrates using let/const bindings to capture session
# outputs and pass them as context to subsequent sessions.

# Define specialized agents for the workflow
agent researcher:
  model: sonnet
  prompt: "You are a thorough research assistant who gathers comprehensive information on topics."

agent analyst:
  model: opus
  prompt: "You are a data analyst who identifies patterns, trends, and key insights."

agent writer:
  model: opus
  prompt: "You are a technical writer who creates clear, well-structured documents."

# Step 1: Gather initial research (captured in a variable)
let research = session: researcher
  prompt: "Research the current state of quantum computing, including recent breakthroughs, major players, and potential applications."

# Step 2: Analyze the research findings (using research as context)
let analysis = session: analyst
  prompt: "Analyze the key findings and identify the most promising directions."
  context: research

# Step 3: Get additional perspectives (refreshing context)
let market-trends = session: researcher
  prompt: "Research market trends and commercial applications of quantum computing."
  context: []

# Step 4: Combine multiple contexts for final synthesis
const report = session: writer
  prompt: "Write a comprehensive executive summary covering research, analysis, and market trends."
  context: [research, analysis, market-trends]

# Step 5: Iterative refinement with variable reassignment
let draft = session: writer
  prompt: "Create an initial draft of the technical deep-dive section."
  context: research

# Refine the draft using its own output as context
draft = session: writer
  prompt: "Review and improve this draft for clarity and technical accuracy."
  context: draft

# Final polish
draft = session: writer
  prompt: "Perform final editorial review and polish the document."
  context: draft