A technical series on what separates agentic AI prototypes from production systems — drawn from engagements across healthcare, government, and enterprise software.
Most organizations are building AI agents. Most of those systems will not reach production — not for lack of capability, but because production systems require reliability, observability, cost predictability, regulatory compliance, and diagnostic depth that demos do not.
This series is a technical field guide for teams working across that gap. Each post covers a specific, concrete challenge — with architectural patterns, trade-offs, and implementation detail.
Written from the experience of shipping agentic systems that run in production.
The same design choices that make a demo compelling often introduce fragility at scale. A review of the common failure modes and the architectural principles that address them.
How to establish quantitative confidence that a new model version or prompt change improves rather than degrades system behavior, without relying on manual inspection.
Observability, cost attribution, and architectural patterns that keep inference spend predictable — including a reference architecture for per-workflow cost tracking.
When to distribute work across agents, how to pass context safely between them, and the operational failure modes of architectures that appear elegant at design time.
LLMs fail in ways that deterministic software does not. Retry logic, fallback strategies, and circuit breaker patterns that produce graceful degradation and actionable diagnostics.
The security posture for agentic AI differs substantially from traditional web applications. A review of the real risks — prompt injection, data exfiltration, tool abuse — and architectural controls that address them.
The architectural decisions that determine whether a healthcare AI system passes a compliance audit — and why those decisions must be made at design time rather than retrofitted.
Technical success is a necessary but insufficient condition for adoption. A framework for deploying AI agents into real workflows — adoption, trust, feedback loops, and rollback criteria.
No marketing content. No newsletter cadence. The series as it is published.