Vinay Chaudhari

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Thoughts on AI, ML, and building intelligent systems

AI EngineeringAgentic AIContext EngineeringModel Context Protocol

AI in 2026: Are You Still Learning It Like It's 2022?

In 2022, AI was about prompts and LLM answers. In 2026, the challenge is engineering reliable systems: context engineering, multi-agent orchestration, MCP, state management, guardrails, evals, and observability. Here is the modern 9-layer AI stack.

Sep 25, 20268 min read
Read on Hashnode
What's inside17 sections
  • 01The Shift: Prompt Engineering → Context Engineering
  • 02From Chatbots to Goal-Oriented Autonomous Agents
  • 03RAG → Agentic & Hybrid Retrieval Decision Loops
  • 04Function Calling → Broad Tool Use & Safe Execution
  • 05APIs → Model Context Protocol (MCP Standard)
  • 06Single Agent → Multi-Agent Systems & Handoffs
  • 07Agent-to-Agent (A2A) Protocol & Interoperability
  • 08Conversation History → Memory, State & Checkpoints
  • 09Final Answer Evaluation → Trajectory & Workflow Evals
  • 10Logs → Full-Stack Agent Observability & Tracing
  • 11Safety Filters → Deterministic Guardrails & Approvals
  • 12Bigger Models vs. Better System Architecture
  • 13Model Selection → Cost- and Latency-Aware Routing
  • 14Token Optimization → AI Cost Engineering
  • 15Code Generation → Sandboxed Execution Environments
  • 16The 2026 AI Engineering Stack: 9-Layer Blueprint
  • 17What to Learn Now: 10 Core Architectural Capabilities
FastAPILangChainDockerLLM

FastAPI + LangChain + Docker: My Blueprint for Shipping LLM Microservices to Production

A working architecture for turning an LLM prototype into a production-ready microservice — covering streaming, semantic caching, automatic provider fallback, and a real Kubernetes deployment path.

Jul 18, 20267 min read
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What's inside18 sections
  • 01The gap between 'the model works' and 'the service survives real traffic'
  • 02End-to-End Architecture: FastAPI → LangChain → Docker → Kubernetes
  • 03Streaming FastAPI Endpoints with astream (not blocking invoke)
  • 04Health Probes and Pydantic Input Validation
  • 05Per-Session Rate Limiting to Protect LLM Budget
  • 06Semantic Caching with RedisSemanticCache (catch near-duplicate queries)
  • 07Automatic Provider Fallback with .with_fallbacks()
  • 08Environment-Driven Model Configuration (no hardcoded versions)
  • 09RAG Retriever Integration Point in the Chain
  • 10Multi-Stage Docker Build for Lean, Secure Images
  • 11Non-Root Container Users and HEALTHCHECK
  • 12docker-compose for One-Command Local Development
  • 13Kubernetes Deployment: Resource Limits and Liveness/Readiness Probes
  • 14HorizontalPodAutoscaler for Traffic Spikes
  • 15CI/CD Pipeline: Lint → Test → Build → Push → Deploy
  • 16Observability with Langfuse / LangSmith
  • 173 Real Mistakes: Caching, Single Provider, Bloated Images
  • 18FAQ: Kubernetes vs. docker-compose, Self-host vs. API, Cost Control
AI EngineeringProduction LLMsLLM LatencyRAG Architecture

Prompt Engineering Got You a Demo. Here's Why Your AI Will Fail in Production

Prompt engineering gets you a demo. This guide covers everything you need to actually ship AI in production.

Jun 30, 202615 min read
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What's inside22 sections
  • 01Harness Engineering vs. Prompt Engineering
  • 02Context Engineering vs. Long Prompts
  • 03Caching: Prompt Caching, Semantic Caching, and the KV Cache
  • 04Prefill vs. Decode: Why They Optimize Differently
  • 05Continuous Batching, Paged Attention, and Throughput
  • 06Speculative Decoding, Quantization, and Distillation
  • 07Quantization Formats: INT8, INT4, FP8, AWQ, GPTQ
  • 08Structured Output Failures and Repair Loops
  • 09Function Calling Reliability and Tool Contracts
  • 10Agent Guardrails: Loop Budgets and Termination
  • 11Model Routing and Graceful Degradation
  • 12RAG Architecture: Chunking to Reranking
  • 13Retrieval Evals: Recall, Precision, Grounding
  • 14Evals as a Discipline
  • 15LLM Observability
  • 16Cost Attribution Beyond Per-Model Pricing
  • 17Safety Engineering: Injection, Leakage, Permissions
  • 18Multi-Tenant Isolation
  • 19Fine-Tuning vs. ICL vs. RAG vs. Distillation
  • 20Tradeoffs Across the Inference Stack
  • 21Production Failure Modes
  • 22FAQ
Agentic AIAI AgentsCode ExecutionPython REPL

Multi-Agent Dead Reckoning: The Hidden Failure Mode in Vibe Coding Systems in 2026

Most multi-agent AI systems do not fail immediately—they drift slowly. This post explains why coordination drift happens and how to prevent it.

May 7, 20268 min read
Read on Hashnode
What's inside15 sections
  • 01What Is Multi-Agent Dead Reckoning?
  • 02The Handoff Trap: Trusting Previous Agent Outputs
  • 03Three Main Failure Modes: Cascade, Topology, Consensus
  • 04Cascade Amplification: How Small Errors Propagate
  • 05Topological Sensitivity: How Structure Affects Reasoning
  • 06Consensus Inertia: The Danger of Shared Bad Context
  • 07Why Long-Running Workflows Amplify Drift in 2026
  • 08The Solution: Explicit Re-Anchoring Loops
  • 09Role Isolation: Separate Planner, Executor, and Reviewer Roles
  • 10Periodic Objective Reset: Re-Stating Goals Mid-Workflow
  • 11Adversarial Debugging: Implementing Contradiction Agents
  • 12Semantic Validation: Checking Intent Over Simple Outputs
  • 13Implementation: Building a Simple Drift Check Function
  • 14Debunking Myths: Scaling Agents vs. Scaling Coordination
  • 15Debugging Social Failures: Tracking Information Propagation
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