Practical guides for teams building AI products, integrating business systems, and making software dependable in use.
Written by Satish Rajnale, Senior AI Product Engineer.
Find the right business process to automate and design a workflow with scoped tools, idempotent actions, approval checkpoints, retries, audit logs, and measurable outcomes.
A practical guide to choosing an agent use case, connecting business tools safely, and planning the reliability, security, evaluation, and maintenance work around an AI agent.
Plan a useful AI SaaS MVP with a stack that fits the product, tenant-safe data boundaries, observable model usage, and a scope you can estimate without invented cost promises.
Compare managed search, custom retrieval-augmented generation, and hybrid systems, with practical guidance for permissions, freshness, citations, evaluation, and data lifecycle.
A practical playbook for offline evals, online acceptance metrics, HITL boundaries, and tightening autonomy only when the product earns it.
Designing durable AI workflows with checkpoints, idempotent tools, Temporal-style recovery, and human takeover — so partial failure does not become silent corruption.
How I design context layers — working memory, canonical state, retrieval, and provenance — so AI features stay useful as products grow.
How I treat tokens as latency, cost, and quality budget when designing AI features — caching, compression, routing, and saying no to context bloat.
What I learned shipping agent workflows end to end — from customer jobs and capability limits to tool graphs, state, evals, and post-deploy ownership.