Production-Ready Agents: Building Reliable Autonomy in Healthcare

Building an autonomous agent may only take a few lines of code thanks to the pace of current AI progress, but deploying one in production, especially in healthcare, requires different thinking.

As 'Grandfather of AI' Andrew Ng, noted: "Hardly any of today's practical, commercially valuable agentic workflows were built using this simple approach... Building a reliable agent today requires much more scaffolding to guide it."

In the case of a medication adherence outreach system we helped deliver, making an LLM reason about patient data was the easy part but making it reason reliably, at scale, in a regulated environment was definitely the challenge.

The Production Challenge

The gap between "agentic demos" and "agents in production" comes down to control. It’s not just giving an LLM carte blanche database access and letting it decide what to do. Making good quality recommendations based on real patient data requires:

• Deterministic data flows (what the agent sees and when)
• Constrained reasoning (complex analysis within defined parameters)
• Human oversight (experts reviewing outputs before action)
• Audit trails (understanding exactly what led to each decision)

Architecture Over Autonomy

While building this agentic system to generate outreach recommendations for improving medication adherence, each component needed bounded autonomy appropriate to its risk profile:

• Scheduler triggers batch processing on defined intervals
• SQS queue delivers structured patient data (medical profile, communication history, adherence levels, clinical guidelines)
• Worker consumes queue and prompts an LLM reasoning model
• Outputs route to Snowflake for expert review in web application
• Approved recommendations flow to appropriate channels

This orchestration architecture controls when agents engage, what data they access, and how outputs move through the system. The reasoning happens autonomously; the execution doesn't. Additionally, this setup allows for connecting other narrow agents like voice and email to operate in more precise use cases like virtual outreach to assess health risk and adherence barriers.

Why Bounded Autonomy Works

The key benefits to this level of production engineering are:

• Reliability: Deterministic pipelines mean predictable behavior, something that is harder to achieve when agents are given full access to everything.
• Risk-appropriate autonomy: Recommendation engine has high reasoning complexity but low operational risk (scheduled runs, controlled data). Voice or email agents would have high interaction complexity but operate within clinical protocols.

The key insight: Production agents are less about maximum autonomy and more about the right autonomy for each component, with scaffolding that ensures reliability and future flexibility while preserving the reasoning capabilities that make LLMs valuable.