Blog
Field notes on shipping AI-native systems with human engineering judgement: architecture, modernisation, automation, and the tradeoffs in between.
Match models to tasks to cut AI costs-use SLMs for structured, high-volume work and reserve GPT‑4 for complex or multilingual cases.
Enterprises require SLMs for data sovereignty, compliance, security, and cost savings; vendors must offer self-hosted, VPC, or on‑prem options.
Architecture, cost, quantization, and compliance trade-offs for self-hosting language models in regulated industries.
Pick the right enterprise LLM by matching precision, context length, and throughput to your workflow.
Stress-test agent frameworks early: add observability, reproducible tests, run five critical stress tests, and know when to switch.
Why multi-agent systems fail in production: deadlocks, cascading errors, context loss, poor observability, and exploding token costs.
How week-one memory choices set an AI agent’s scalability; compare in-context, vector, episodic, semantic memories and tiered designs.
Tool-call failures compound in multi-step agents; robust architecture, deterministic routing, and strict validation are the real drivers of production reliability.
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.