Posts tagged Solution Architecture — AI × software engineering, made practical.
You'll learn how to design a run record that captures every non-deterministic input to an agent run so you can replay failures exactly, and what to look for when you build one or buy one.
You will learn to sort a change into reversibility classes R0 to R3 and then pick a rollout pattern that still protects you when rolling back cannot undo the damage, including for AI agents.
You will learn why blue-green rollback stops working once shared state changes shape, how to measure your real rollback window, and how to use expand/contract migrations, N-1 compatibility checks and tolerant readers so a cutover is safe to roll back.
You'll learn how to find the slowest consumer in an agentic pipeline (usually the human reviewer), use Little's Law to estimate queue wait times, and design backpressure with a bound, an upstream signal and a defined producer response.
You will learn how to decide which agent work to drop under overload, using deadline budgets, a shedding ladder, retries at a single layer, retry budgets and priority tiers with a named owner.
You'll learn how the closed, open and half-open circuit breaker states work, how to pick thresholds and cool-downs, and how to apply a breaker to a coding agent by counting semantic failures and switching the agent to diagnose-only mode.
Event-driven architecture never actually removes the dependency between your services — it just deletes the paper trail, and the same blind spot is already sitting inside your multi-agent pipelines.
The cache you turned on is working exactly as designed — it's just guarding the one line item on your invoice that was never the problem.
Every replica in your fleet is passing its health check while real requests fail, and once you see why, you'll never trust a green dashboard the same way again.