Agentic Development
Built for
Founders.

# Discovery session output
business_map = analyze_operations(founder_input)

# Identify automatable processes
agents_needed = []
for process in business_map.manual_tasks:
  if process.frequency > "daily":
    agents_needed.append({
      task: process.name,
      complexity: process.decision_complexity,
      autonomy: "full" if process.risk < "medium" else "supervised"
    })

# Architecture blueprint
blueprint = design_agent_system(agents_needed)
print(f"Agents designed: {len(agents_needed)}")
print(f"Estimated time savings: {blueprint.hours_saved}/week")

# Week 1: Core agent framework
agent = Agent(
  model="claude-sonnet",
  tools=[knowledge_base, crm, email],
  memory=ConversationMemory(window=20),
  guardrails=SafetyLayer(max_actions=50)
)

# Week 2: Integration & testing
agent.connect(your_api)
agent.load_knowledge(your_docs)

results = agent.run_test_suite(
  scenarios=real_customer_queries,
  success_threshold=0.95
)
# → 97% accuracy on test scenarios

# Ship to beta users
deploy(agent, env="staging")
beta_users = select_cohort(size=50)

# Monitor & iterate
for week in range(1, 5):
  metrics = collect_metrics(agent)
  print(f"Week {week}:")
  print(f"  Tasks handled: {metrics.tasks_completed}")
  print(f"  Success rate:  {metrics.success_rate}%")
  print(f"  Escalations:   {metrics.escalation_rate}%")

  # Tune based on real data
  if metrics.escalation_rate > 10:
    optimize_prompts(agent, metrics.failed_cases)
    add_guardrails(agent, metrics.edge_cases)

# Production deployment
deploy(agent, env="production",
  scaling=AutoScale(min=1, max=100),
  monitoring=FullObservability(
    traces=True,
    token_tracking=True,
    cost_alerts=True
  ),
  fallbacks=HumanEscalation(
    threshold="confidence < 0.8"
  )
)

# Result: Founder's daily ops report
📊 Agent Performance (last 24h)
✅ 847 tasks completed autonomously
⚡ Avg response time: 1.2s
💰 Cost: $12.40 (vs $2,100 human equiv)
🎯 Customer satisfaction: 4.8/5