This repository hosts my personal portfolio. The best way to experience it is live, not as source files.
For 5 years I worked enterprise IT operations: SAP and ServiceNow, strict SLAs, the receiving end of broken software at HCL Technologies and EvoraIT. I learned what production discipline actually means.
Then, about 2 years ago, I started experimenting with AI on my own. No bootcamp, no team, no roadmap. I began by building small AI apps, taught myself as I went, and kept pushing until the apps became full systems.
The further I went, the harder the real problems got. Not "how do I call an LLM" problems, but systems problems:
- Memory. My agents forgot everything between sessions. There was no continuity, no way to reuse what they had already learned.
- Context. Agents wasted thousands of tokens re-reading entire codebases. Context windows blew up and so did the cost.
- Reliability. A single-provider setup died the moment one API rate-limited or went down.
- Consistency. Multi-agent pipelines drifted, contradicted themselves, and repeated the same failures in loops.
I solved each one inside my own codebases, then optimized: multi-provider routing with failover, persistent reality-first memory, context graphs to cut token waste, response caching, and guardrails against prompt injection and repeat failures.
Once those patterns were stable, I extracted them out of my private systems and open-sourced them, so other self-taught builders do not have to learn it all the hard way. This site is the showcase of that work.
2019 - 2022 HCL Technologies, Noida
L1 Agent to SME to Team Lead to Acting Floor Manager
250+ agents trained, new support vertical live in 6 months, 4 promotions
2022 - 2024 EvoraIT Solution Pvt Ltd, Gurugram
SAP L2 Support Specialist
350 tickets/week, 98% SLA compliance, 95% MTTR improvement
2024 - Now Self-taught AI engineering
From first AI experiments to production systems and open-source patterns
I am open to Forward Deployed Engineer, Technical Solutions Engineer, and Founding Engineer roles at API-first AI startups.
19 repositories · 261+ automated tests · 3 shipped products · 5+ years ops & SLA ownership
Full case studies, architecture diagrams, and live demos on the portfolio site — which itself runs a live RAG chatbot grounded on these repos (ask it anything).
| Product | Stack | Status |
|---|---|---|
| WellnessInYou + BODH — Full-stack AI wellness platform | Next.js 15, Expo 54, CrewAI, Gemini, Razorpay/Stripe | Live |
| Agentic SDLC — 6-agent software development pipeline | Node.js, SQLite Blackboard, multi-LLM router, React+Vite | Private beta |
| Agentic OS — Multi-agent outreach + content system | FastAPI, Redis, LangChain, Instagram automation | Internal |
| Adiyogi Books — Independent publishing brand | Next.js, Prisma, Razorpay | Live |
Foundations for anyone building agent systems from scratch.
| Repo | What it teaches |
|---|---|
| AI-Systems-Evolution | The same task at 6 levels of autonomy — code → single call → workflow → agent → team → swarm |
| Agent-Anatomy | One agent, four removable organs (brain/hands/memory/loop). Toggle one off, watch it break |
Production patterns extracted from real systems. Each repo isolates one hard problem.
| Repo | Problem solved | Tests |
|---|---|---|
| Agent-Scars | Failure memory — agents that learn from past mistakes | 7 |
| Agent-Context | Dependency graphs to cut token waste on large codebases | 6 |
| Agent-Recall | Cross-session solution memory with similarity retrieval | 9 |
| Agent-Routing | Multi-provider LLM failover (NVIDIA → Groq → OpenRouter → Gemini) | 23 |
| Research-Agent | Web research + multi-source summarization pipeline | 9 |
| Content-Analyzer | URL → structured JSON extraction with AI analysis | 4 |
| Video-Engine-Starter | Text-to-video orchestration with Remotion + AI directors | — |
| Agent-Constitution | Drift detection + anti-hallucination guardrails | 6 |
| MCP-Agent-Toolkit | MCP server with blackboard, SCAR, and cache tools (node:sqlite) | 13 |
| RAG-Knowledge-Engine | Hybrid retrieval (BM25 + vector RRF) + cross-encoder rerank + RAGAS eval | 25 |
Complete systems and production templates.
| Repo | What it is | Tests |
|---|---|---|
| Agentic-Patterns | 7 architectural guides with runnable starters | — |
| Agentic-Systems | 5 complete standalone agent systems | — |
| AgentKernel | 6 production engines in Python + JavaScript | — |
| Agentic-SaaS-Boilerplate | Billable multi-agent SaaS template — real WorkflowEngine, SSE queues, AgentRegistry | 22 |
| Problem | What I built to solve it |
|---|---|
| Agents forgetting across sessions | Reality-first persistent memory with cross-session reuse |
| Token waste from re-reading code | Dependency context graphs and layered RAG filtering |
| Single point of failure on LLM calls | Multi-provider router with circuit breakers and ordered failover |
| Brittle linear agent chains | DAG orchestration with Kahn's topological scheduling |
| Agents drifting and repeating failures | Anti-drift reality files and a repeat-failure guard (SCAR) |
| Prompt injection and secret leakage | Input and output guardrails, tested against real payloads |
| RAG returning irrelevant chunks | Hybrid BM25 + vector retrieval with RRF fusion + cross-encoder rerank |
| LLM provider downtime | 5-provider failover chain — any key missing is silently skipped |
Static site. No framework, no build step. Hand-written HTML, CSS, and vanilla JavaScript with inline animated SVG diagrams. Loads in under a second. The portfolio chatbot runs as a Vercel serverless function — Gemini embeddings over a prebuilt 118-chunk index, generation on a 5-provider failover chain.
- Email: Shubham.prajapati086@gmail.com
- LinkedIn: linkedin.com/in/shubham-prajapati086
- Location: Alwar, Rajasthan. Open to onsite Gurugram/NCR and remote globally