Mastering Agentic Engineering for Reliable AI SystemsMastering Agentic Engineering for Reliable AI Systems
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Agentic engineering isn’t “build a chatbot.” It’s building a system that can take an intent, plan steps, call real tools, and finish the job reliably.
Textpro.ai is my favorite example. The goal was simple: let a user ask in plain English for something like a flight, ticket, or service, and actually complete the transaction through real vendor APIs and Stripe. https://contra.com/p/o6ekBlNo-textproai-ai-universal-concierge-platform?referralExperimentNid=DEFAULT_REFERRAL_PROGRAM&referrerUsername=brian_pyatt_57qklx9q
What made it “agentic” wasn’t the model. It was the orchestration:
explicit workflow states (collect → quote → confirm → pay → execute)
strict structured outputs (schemas, validation)
retries + timeouts for flaky APIs
idempotency keys so you never double-charge or double-book
clear failure paths (“we’re missing X” vs hallucinating)
The biggest lesson: agents only become production-grade when you treat them like distributed systems. The LLM can propose, but your code must control execution.
If you’re building agents, don’t start with prompts. Start with state, tools, and guardrails. That’s where reliability lives.
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