Blog.

EPIC is the communication framework I use in every client session. Four moves: Express WHY, Point first, Illustrate with stories, Close on commitment. No fluff.

CrewAI vs LangGraph isn't a capability debate — it's a mental model debate. CrewAI thinks in people. LangGraph in process. Here's which one fits your problem.

Three wrong turns building a LangGraph agent — chained functions, while loops, and WebSockets for progress — and the production fixes that actually ship.

Naive JSON prompting fails 5-15% in production. Every major LLM provider ships constrained decoding now — the model cannot violate your schema. Here's how.

Claude Fable 5 survived 1,000 hours of bug bounty testing. A multi-agent pack hunt bypassed it in 48 hours. Here's why classifiers can't see the whole attack.

Z.ai shipped GLM-5.2: 744B MoE, 1M context, MIT license, zero benchmarks at launch. Here's what the architecture means and why the silence is the real story.

I built a configuration audit tool for Oracle Fusion ERP and deliberately left out the LLMs. Here's why deterministic pipelines win in regulated environments.

IT audit has 40 years of maturity. Then agentic AI arrived — LLMs making tool calls with real-world side effects — and the frameworks ran out of answers.

India has ₹12+ lakh crore in pending TP disputes at ITAT. A traditional TP study costs ₹20–50 lakh. Aura TP uses adversarial agent ensembles — Inclusion Counsel, Exclusion Counsel, and a Partner Arbitrator — to automate comparable selection review and TPO defense drafting. Here’s the architecture, what breaks, and the honest cost math.

EU AI Act high-risk obligations are live. 40% of enterprise systems can't be classified. Here's what boards, engineers, and deployers actually need to do about it.

Voice is the default UI for agentic systems in 2026. Here's the full infrastructure stack: STT engines, TTS providers, WebRTC vs WebSockets, and where your 570ms latency budget actually goes.

MMLU is saturated noise. SWE-bench Pro is the real signal. Here's how to actually evaluate models — benchmarks that matter, the fine-tune vs RAG vs few-shot question, and what each model tier is actually for.

Most LLM teams discover semantic caching too late. Here's how the three-layer architecture works, why the threshold matters more than you think, and what real production hit rates look like.

Groq vs Bedrock vs Azure OpenAI vs self-hosted vLLM — when each provider actually makes sense, what the compliance tradeoffs look like, and where the $80K/month break-even comes from.

At 95% per-agent reliability, a 10-step sequential chain succeeds 60% of the time. What actually breaks in production multi-agent systems — and what LangGraph, AutoGen, and CrewAI don't tell you.

What breaks when your AI chatbot hits 1,000 concurrent users — and how to fix it before it does. Stateless design, semantic caching, gateways, and streaming at scale.

The real failure rate of RAG in production is 26.4% — not 2.3%. Here's what actually breaks: chunking, retrieval drift, context stuffing, and silent hallucinations. And how to fix it.

After Google I/O 2026 announced AP2 and Universal Cart, I spent a weekend making tanmaybohra.com callable by Gemini, Claude, or any MCP-compatible AI. Here's exactly what I built — and where the ecosystem actually stands.

Replit tried fine-tuning and abandoned it. The real architecture behind the best AI coding agents is a curated store of high-quality traces, retrieved at inference time. Here's how it works — and when fine-tuning, Agent Skills, or inference-time compute is actually the right answer.

Sourcegraph got a $1M cloud bill three months after launch. Fixie's agent looped 847 times before anyone noticed. These aren't edge cases — they're the default outcome of shipping agents without treating observability and cost as first-class concerns.

A naive 7-step agent with 3% per-step failure has an 80% end-to-end success rate. Inngest's step-level durability raises that to 99.98% — and eliminates the need to hand-build retry logic, human-in-the-loop state machines, or observability tooling from scratch.

Developer life stays close to your own team - until an optional hobby group quietly puts you in the same room as people who have been losing sleep over problems you never knew existed.

State machines, retry logic, and the edge cases that make agentic workflows fall apart at scale. Three months of production LangGraph — the parts the docs skip over.

Partial indexes, covering indexes, and when BRIN beats everything — built from real EXPLAIN ANALYZE output, not toy examples. The strategies that cut query time from 4.8s to 12ms.

TypeScript's type system vanishes at runtime. Zod bridges that gap — but the naive integration adds overhead you don't need. Here's how we use it at scale without paying the parse tax.

The naive goroutine-per-request model looks fine until 50k connections hit simultaneously. Here's the worker pool pattern I actually use in production, and why it cut our memory by 80%.