I am a Senior Agentic AI Engineer specializing in the orchestration of autonomous, goal-oriented AI systems. My work focuses on moving beyond simple chat interfaces to building production-grade agentic workflows that possess reasoning, planning, and self-healing capabilities. I architect multi-agent systems that solve complex, non-linear problems at scale.
- 🔭 Currently Architecting: A distributed multi-agent swarm for automated software engineering.
- 🧠 Core Expertise: LLM Reasoning (CoT/ToT), Agentic RAG, Tool-Use Optimization, and Multi-Agent Orchestration.
- 🌱 Deep Diving: Exploring the intersection of Small Language Models (SLMs) and edge-based agentic reasoning.
- 👯 Open Source: Active contributor to aden-hive/hive (Agentic Framework Internals).
- 💬 Consulting: Helping enterprises transition from "Chatbots" to "Agentic Workforces".
I build production agentic AI systems where failure is handled, not hoped away. While most systems are designed for the "happy path," I design for the rate limits at step 3, the approvals that arrive 4 hours later, and the platforms that redesign their UI overnight.
- Single Responsibility: Every agent answers exactly one question to isolate failure modes.
- Deterministic Routing: Routing is pure Python, not LLM-driven, for testability and version control.
- Mandatory Verification: Post-action verification is a hard checkpoint before state advances.
- Durable HITL: Human-in-the-loop is a named graph node that survives process restarts and deployments.
- Architectural Boundaries: Action limits are compile-time constants, not prompt-level suggestions.
- Hybrid Retrieval: Dense + sparse retrieval with cross-encoder re-ranking for real-world query variance.
These patterns are the foundation of every production system I build, ensuring reliability, security, and resilience in non-deterministic environments.
| Pattern | Description | Justification |
|---|---|---|
| PAV (Perception-Action-Verify) | A vision-guided loop where a PerceptionAgent analyzes screenshots to guide actions and verify outcomes. | Eliminates reliance on brittle CSS selectors; ensures the agent "sees" the result of its work. |
| UNVERIFIABLE State | A first-class state for actions that executed correctly but cannot be confirmed due to propagation delays. | Prevents duplicate actions from retries and maintains system honesty during async processing. |
| API-First with Browser Fallback | Automatic transition to browser automation when API endpoints fail or are unavailable. | Ensures 100% task completion regardless of external API stability or coverage. |
| Capability Self-Calibration | Real-time reliability tracking that auto-disables/re-enables tools based on health checks. | Prevents cascading failures and optimizes routing toward the most stable execution path. |
| Audit Hash Chains | Append-only logs where each row contains a SHA-256 hash of itself and the previous row. | Provides tamper-evident proof of every agent action and decision for high-stakes environments. |
| Category | Tools & Technologies |
|---|---|
| Agent Frameworks |     |
| LLMs & APIs |     |
| Vector DBs |     |
| Backend & Infra |     |
| Monitoring & Eval |    |
- AI Product Red-Team Agent: LangGraph · AutoGen · Claude API · FastAPI · Redis. Lethal Trifecta injection · CVSS-scored vulnerability reports.
- Automated Code Compliance & Security Agent: AWS Bedrock · Guardrails · GitHub Webhooks · Terraform. 100% policy adherence · 60% reduction in code review time.
- Agentic Supply Chain Orchestrator: Azure AI Foundry · RAG · GPT-4o · 5 agents. BOM → PO with zero manual steps · 100% error detection.
- Autonomous QA Agent Framework: LangGraph · ChromaDB · GitHub Actions · pytest · 8 agents. Full test lifecycle: PR diff → generation → self-healing → defect filing.
- Self-Healing Selenium Framework: LangGraph · Selenium · PostgreSQL · 6 agents. DOM-aware repair · confidence-gated HITL · autonomous commit on high confidence.
- Agentic Defect Intelligence Pipeline: LangGraph · Qdrant · Kafka · hybrid RAG · 6 agents. Closed-loop: production failures → pre-release test coverage feedback.
- Multi-Agent User Simulation & Chaos Lab: LangGraph · LangChain · FastAPI · 4-layer architecture. Persona generation · chaos injection · behavioral anomaly detection.
Python · Agentic Framework Internals
- Impact: 9 core contributions across bugs and features.
- Key PRs: #5923, #5855, #5918, #5760, #5805, #6214, #6542, #6555, #6605.
- 🎓 MSc in AI, Agentic AI & LLM Engineering — Woolf University (in progress)
- 🎓 BSc Computer Science — Menoufia University
- ☁️ AWS AI/ML Scholar 2025
"The best way to predict the future is to build an agent that creates it." — AI Proverb