Automation Consultant - Agentic AI Reliability Focus

I build reliable AI agents with the discipline of test automation.

My automation background is the advantage: repeatable execution, strong signals, failure isolation, and evidence-first delivery for agentic AI systems.

My professional role remains Automation Consultant. In parallel, I am building an agentic AI reliability direction from that foundation: local-first run capture, validation workflows, LLM test harnesses, and failure postmortems that make AI-assisted engineering easier to inspect and improve.

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Why automation matters

Agents need more than prompts.

They need the valuable engineering habits automation already taught us: repeatability, observability, fixtures, boundaries, and failure evidence.

Where I specialize

Reliability systems for agentic AI.

I build tools around agent runs: capture what happened, redact sensitive context, classify failures, replay behavior, and produce postmortems teams can act on.

What teams get

AI workflows that can be trusted.

The result is agentic automation that is easier to debug, safer to scale, and more useful under production pressure.

Behind the Code

My work sits at the intersection of test automation and agentic AI reliability. After years of building test frameworks, stabilizing brittle browser flows, debugging CI failures, and turning ambiguous defects into reproducible evidence, I apply the same engineering discipline to AI-agent validation, run observability, and reliability tooling.

Agent testing is not just prompt evaluation. Reliable agentic systems need observability, replayable evidence, failure taxonomies, browser/runtime signals, safe redaction, and postmortems that explain risk. That is the bridge I am building through Agent Blackbox and related reliability tooling.

"Reliable agents need the same discipline that made reliable automation possible.|

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Dhiraj Das
🔬 Research Contribution

Co-Author of The TDCT Technique

Developed the Triangle-Density based Clustering Technique (TDCT), a novel density-based clustering method for spatial data analysis.

💡 See how this research shapes my current automation logic →

* Published in IOSR Journal of Computer Engineering (IOSR-JCE), Vol 3, Issue 6.

🎮 Interactive Game

Master Locator Strategies

Gamified learning experience to master XPath and CSS selectors. Solve puzzles, level up, and sharpen your automation skills.

📖 Free Resource

The Automation Architect's Playbook

A comprehensive guide to enterprise-grade test automation, authored by me. Master browser internals, eliminate flaky tests, and learn AI-powered testing.

🌌 Open Source Protocol

Starlight Protocol

Resilient browser automation through autonomous Sentinel coordination. A formal, open standard for building self-healing test systems.

Automation Roots, Agentic AI Direction

Ten years of automation work shaped the habits I now bring to agents: observe the run, control the inputs, isolate the failure, and prove the fix.

10+
Years
Experience
~35%
Avg
Efficiency Boost
High
Impact
Cost Savings
7+
Years
Global Exp

Career Timeline

Automation Consultant

Present

Consulting on automation strategy and quality systems while independently building reliability tooling for AI-assisted engineering: agent run capture, validation workflows, and failure postmortems.

Extending automation discipline into observable, repeatable, evidence-backed agent workflows

  • ▹ Designing Python-first tools for agent diagnostics and postmortems as an independent focus.
  • ▹ Applying CI, browser automation, and failure-triage patterns to AI-agent runs.
  • ▹ Defining guardrails for reliable local-first and AI-assisted workflows.
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Senior Automation Developer

7 Years

Built and stabilized large web, API, and mobile automation programs across high-pressure delivery environments.

Reduced flaky failures by 70% across 200+ test suites

  • ▹ Managed complex web, API, and mobile automation suites.
  • ▹ Worked with diverse tools like UFT, AutoIt, and QF Test.
  • ▹ Delivered critical automation solutions for major corporate clients.
  • ▹ Gained deep Airline Domain Expertise.
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Junior Automation Developer

3 Years

Built the foundations: reliable Selenium suites, CI integration, maintainable test design, and close QA-engineering collaboration.

Compressed manual regression from 2 weeks to 3 days

  • ▹ Developed test scripts using Selenium WebDriver and Java.
  • ▹ Learned CI/CD integration and version control best practices.
  • ▹ Collaborated with QA teams on manual-to-automation transition.
  • ▹ Built foundational expertise in test design patterns.
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Tech Arsenal

Pick the reliability gap: opaque agent runs, flaky CI, Cloudflare walls, login overhead, visual drift, or GenAI UIs. I build tools for the places normal automation and naive AI workflows break.

Agentic AI and Automation Systems

Practical tools that show the bridge from automation to agentic AI: reliable runs, explainable failures, safer LLM integrations, resilient browser workflows, and test systems that expose risk instead of hiding it.

Recent Insights

Latest notes on automation, agentic AI, reliability, and engineering

July 01, 2026

Agent Blackbox: A Beginner-Friendly Guide to Agents, Harnesses, and Reliable Agentic AI

A ground-up explanation of what agents are, how agent harnesses work, why agent debugging is difficult, and how Agent Bl...

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June 28, 2026

Mixture of Agents, Explained Simply: How Hermes Uses Multiple Models

Hermes Agent's Mixture of Agents mode lets one agent ask multiple models, then use an aggregator model to combine the be...

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June 27, 2026

From Plain Text to Beautiful Timelines: Launching the Visual Flight Recorder

Terminal logs are hard to parse when debugging autonomous agent loops. We designed and built an interactive, local-first...

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