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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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The fix for an agent that keeps getting your design system wrong isn't a better prompt.
It's putting the rules where the agent reads them.
In the repo. In skills. In checks it can run itself.
Writing a course about exactly this.
What sells it for me is the refusal telling the agent exactly where the token should go. A bare "no hex" error usually just sends it looping. Does guard.mjs run only as an agent hook, or in pre-commit too so it catches humans as well?
Testing two color directions for Pilotly, an AI agent platform for product teams.
Which direction would you pick for an AI automation product?
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I'd pick Violet Desk. Blue is what every SaaS uses, violet stands out and still says AI, and the orange sticky notes pop more against it. Does the hero chip row under the headline work on mobile?
2 minutes to read to understand AI agents vs automation
techfoundry.io
How to Build AI Agents | TechFoundry AI – TechFoundry
Build AI agents that understand context, use business tools and hand decisions back to people. A practical guide to creating agents for real operations.
Nice, thank you for this