Carlos Egana

# The pattern behind every agentic system I ship
async def run_agent(task: Task) -> Result:
    context = retrieve(task, vector_store)   # RAG: ground the model in real data
    plan    = llm.plan(task, context)        # reasoning before acting
    for step in plan:
        tool   = registry.resolve(step)      # tool use via MCP servers
        result = await tool.execute(step.args)  # typed, validated I/O
        context.update(result)               # feedback loop
    return evaluate(context.output)          # evals + guardrails before delivery

# How I approach every AI system
def build(problem):
    solution = (
        understand(problem)   # business context before code
        .simplify()           # smallest architecture that works
        .ship_early()         # production feedback > perfect prototypes
        .measure()            # if it's not monitored, it's not done
    )
    return solution           # reproducible · documented · owned end-to-end