Engineering notes, not explainers.
What we've learned building agentic AI, RAG, and automation systems that hold up in production — written from the decisions we actually made on client work.
Agentic AI
How to Build Human-in-the-Loop AI Agents That Teams Actually Trust
The pattern that made the difference on our production agent deployments: resolve ambiguity toward review, not toward silent action.
6 min read
ReadRAG
Enterprise RAG Architecture: What Actually Breaks in Production
Permission-aware retrieval, citation grounding, and evaluation loops — the three things that separate a RAG demo from a system a company can rely on.
7 min read
ReadAI Automation
AI Automation for Manufacturing Operations: Where It Actually Pays Off
Not every manual process is worth automating. Here's how we prioritize, using two real production deployments as examples.
5 min read
ReadAgentic AI
Building Production-Ready AI Agents with LangGraph
The gap between a LangGraph demo and a production agent is almost entirely about state, persistence, and interrupts — not the model doing the reasoning.
9 min read
ReadRAG
RAG vs Fine-Tuning: What Should Your Startup Actually Use?
The honest answer is usually RAG, occasionally both, and rarely fine-tuning alone — here's the decision framework and what each path actually costs to run.
7 min read
ReadAgentic AI
Designing Human-in-the-Loop AI Agents for Business Workflows
Confidence thresholds, tiered escalation, and SLA timeouts — the routing logic that decides when an agent acts on its own and when it hands off to a person.
8 min read
ReadRAG
Building a Production RAG System with PostgreSQL + pgvector
You probably don't need a dedicated vector database. Here's how HNSW indexing, hybrid search, and connection pooling in Postgres get RAG to production scale.
9 min read
ReadVoice AI
AI Voice Agents with Amazon Connect and AWS Bedrock
What it actually takes to put a speech-to-speech agent into a real contact center — bidirectional streaming, tool calling, and human fallback, not just a demo call.
8 min read
ReadAI Evaluation
Evaluating AI Agents in Production: Accuracy, Cost and Reliability
An agent that's 95% reliable per step succeeds end-to-end only about a third of the time across a 20-step chain — here's how to actually measure whether an agent is ready for production.
8 min read
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