Beyond the LLM: The 7 Steps to Build a Reliable AI Agent

Choosing the LLM is the easy part.

AI agents are 95% plumbing, not prompts.

Most teams obsess over the 5%:
• Which LLM?
• Bigger context window?
• Token cost?
• Benchmarks?

But that’s not where agents fail.

The real work is the other 7 steps:
workflows, routing, memory, tools, retries, logging, evaluation.

You’re not “prompting a model.”
You’re engineering a system.

Because the hardest part is orchestration:

Knowing when to stop, retry, ask for help, or escalate.

Without infrastructure (tools + memory + testing),
you don’t have an AI agent.

You have a chatbot that hallucinates with confidence.

Stop chasing intelligence. Build the glue.

And when the SOTA model updates?

Your agent shouldn’t break.
Your system should adapt.

Which part of the agent pipeline breaks for you most? 👇
(Orchestration, tools, memory, evals, monitoring?)

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👋 I’m Amit Rawal, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered.

♻️ Repost if you believe AI isn’t about replacing us...
It’s about retraining us to think better.

Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

#AIagents #orchestration #AIinfrastructure #systemengineering #AItools