Taking an AI prototype to launch
Turn a useful demo into an operable product with explicit task outcomes, evaluation, rollout gates, recovery and ownership.
A reading path / Reliability
AI reliability and security
Evaluation, authorization, observability and recovery for live AI systems. Guides, a sample planner and reproducible engineering examples.
A live system needs to complete useful tasks within clear boundaries and make failures understandable. Quality, security and operation are related, but they are not interchangeable scores. A fluent answer can be unsupported, and a high task pass rate can hide one prohibited action.
This reading path starts with release evidence, then follows authority through tools and data, and finishes with runtime investigation and recovery. Choose the section that matches the uncertainty in the system. The runnable examples provide small, synthetic tests of specific boundaries rather than claims about an entire live platform.
For a live incident, begin with the actual task state and business outcome before changing the model. For a planned release, begin with the accepted task, critical failures and a representative evaluation set. In both cases, preserve the evidence needed to explain the next decision.
01 / Reliability
Set a release standard
Define the task outcome, evidence and grading method before interpreting a score. Retrieval and judge calibration need their own diagnostic checks. The sample planner explains a limited statistical model; the executable release gate shows why known critical failures remain independent of the average.
AI agent evaluation: a release framework
Define task success, test permissions and side effects, evaluate failure slices, and turn agent evaluations into an inspectable release decision.
RAG evaluation: test retrieval, answers and access
Separate retrieval quality, answer support and permission failures. Use a small worked corpus to understand what each metric establishes—and what it misses.
LLM as a judge: calibration before automation
Use model graders for tasks they can judge reliably. Calibrate against reviewed examples, inspect missed failures and keep critical release rules explicit.
AI evaluation sample planner
Calculate a one-sided zero-failure binomial bound and the trials needed for a target. Check the statistical assumptions before using the result.
Engineering example: an evaluation release gate
Inspect a runnable evaluation release gate that separates average task quality from critical failures, with fixtures you can run yourself.
02 / Reliability
Enforce authority
Identity, resource permissions and approval must remain valid at execution. Retrieved text and tool output are inputs to inspect, not sources of authority. Follow access through context, caches and citations, and verify denied actions against the actual service boundary rather than relying on prompt instructions.
AI agent authorization at the action boundary
Define who can do what to which resource, derive identity from trusted context and recheck authority when an agent action executes.
Human approval workflows for AI agents
Bind human approval to a specific AI agent action. Review scope, expiry, changed state and the authorization needed before execution.
MCP server security: a review checklist
A practical MCP security review covering identity, authorization, tool execution, prompt injection, state handles and evidence for a release.
Prompt injection defenses for tool-using agents
Review prompt injection defenses at the data and tool boundaries, including scoped access, output validation, approval and adversarial tests.
Permission-aware RAG: access before retrieval
Preserve tenant and source permissions through retrieval, context assembly, caching and citations. Treat revocation and index freshness as part of the design.
Engineering example: permissioned retrieval
Run a synthetic retrieval example with tenant and document access checks. Inspect permitted context, denied requests and the boundaries still untested.
03 / Reliability
Operate and recover
Connect the user task to the model calls, tools and waiting states that explain its outcome. A faster route must still meet data and quality requirements. When a write times out, recover the original operation before issuing another mutation. The response-loss example makes that distinction directly inspectable.
AI agent observability: from traces to decisions
Trace task outcomes, tool actions and waiting states to explain failures, control cost and investigate incidents while limiting sensitive data collection.
LLM model routing and fallback
Choose model routes by task evidence, data policy and operating limits. Treat fallback as a tested execution path—not permission to send any workload anywhere.
LLM latency optimization for useful completion
Locate delays across queues, models, tools and approval. Compare latency changes against task quality, cost and reliable completion.
Idempotent AI tool actions and safe retries
Design retries for AI tool actions with operation identity, transactional boundaries and reconciliation. Inspect the actual business effect.
Engineering example: a retry after a lost response
Run a transaction example that commits once, loses the response and recovers the result. Inspect duplicate, conflict and tenant-scope behavior.
From reading to a decision
Turn a failure into a testable requirement.
A trace, a representative task and the expected boundary are useful starting points. Evaluation and hardening work should produce inspectable cases, explicit release conditions and an operating path the product team can maintain.
Explore evals and reliability