Blog
Field notes on shipping AI-native systems with human engineering judgement: architecture, modernisation, automation, and the tradeoffs in between.
State is a contract: use durable checkpointers, reducers, pruning, and schema versioning to make LangGraph agents production-safe.
Benchmarks mislead: agent frameworks often fail in production due to memory drift, cascading errors, and weak recovery under real-world faults.
Criteria and a 2-week test plan to pick the right AI agent framework-reliability, state persistence, observability, coordination, and cost.
A pragmatic guide to prompt design, STT/TTS selection, and three-tier fallbacks for reliable voice agents across eight languages.
11 critical stress tests to ensure enterprise voice agents handle noise, interruptions, networks, PII, load, and failover.
Why VAD fails in real-world voice agents and how hybrid acoustic+semantic methods reduce cutoffs, false triggers, and latency.
Break down where 2-3s voice-agent lag comes from and how streaming VAD/STT/LLM/TTS plus WebRTC cuts perceived latency.
Most enterprise Document AI errors come from OCR, layout and chunking failures - improve extraction pipelines, metadata, and validation.
Compare LangGraph, CrewAI, and AutoGen for production: state persistence, cost, reliability, and scalability.
Manage technical debt across validation, growth, and scale-ship fast, refactor strategically, and secure enterprise readiness.
Technical debt is a deliberate tool - manage, measure, and repay it with traffic-light roadmaps, AI detection, and business-aligned timing.
90-day rescue plan: assess failing code, stabilize production, fix critical bugs, and reduce technical debt for scalable growth.