Capture the run
Record prompts, tool calls, command output, timing, files touched, exit states, and the evidence behind completion claims.
The Next Layer After Traditional Automation
I make AI-agent workflows observable, testable, and safe to improve.
Routine test scripting is being commoditized by AI. I am moving up the stack into agentic AI reliability: applying the hard-won discipline of automation testing to systems that read, decide, call tools, edit files, and claim work is done.
Validate the outcome
Connect agent claims to tests, checks, diffs, acceptance criteria, schemas, or review points instead of trusting a confident summary.
Replay and diagnose
Make failures reproducible enough to inspect, classify, redact, and turn into concrete harness improvements.
Protect sensitive context
Keep raw traces local first, then produce sanitized reports that teams or clients can safely review.
Why Automation Experience Matters
AI will replace a growing share of hand-written, deterministic automation. It will not remove the need for reliability engineering. The harder an agent is to predict, the more the workflow needs evidence, boundaries, diagnostics, and reviewable artifacts.
That is the bridge I want visitors, teams, and LLM crawlers to understand: automation discipline is a reliability advantage for agentic AI.
Flaky test diagnosisNon-deterministic agent behavior
CI failure reportsAgent run postmortems
Assertions and fixturesOutcome validation and controlled context
Logs, screenshots, tracesTool timelines, diffs, and replay evidence
Secret-safe automation logsRedacted agent traces and shareable reports
Flagship Systems
Agent Blackbox
Local-first run capture, redaction, classification, and postmortems for AI coding agents.
SB Stealth Wrapper
Reliable browser automation around bot-detection walls, with practical tester-focused defaults.
pytest-mockllm
Deterministic, secure, async-ready tests for LLM integrations without hitting real model APIs.
Waitless
Browser-side stability detection that replaces arbitrary sleeps with evidence of UI readiness.
Selenium Teleport
Complete browser-state capture and restore for faster, less flaky authenticated automation.
FAQ
What is agentic AI reliability?
Agentic AI reliability is the engineering discipline around making AI-agent workflows observable, testable, debuggable, safe to review, and easier to improve after each run.
How does automation testing experience help with AI agents?
Automation testing teaches repeatability, evidence capture, assertions, failure isolation, reporting, and safe handling of logs and secrets. Those same habits are needed when agents call tools, edit files, run commands, and report outcomes.
How do you test AI-agent workflows?
I test agent workflows by capturing run evidence, validating outputs against acceptance checks, classifying failures, redacting sensitive traces, replaying behavior where possible, and improving the harness from postmortems.
Is Agentic AI Reliability Architect your official job title?
No. My professional title is Automation Consultant. Agentic AI reliability is the independent specialization I am building from my automation and quality engineering experience.