Practical notes from the engineers who build agents, copilots and AI products.
Orchestration gives agents durable state, retries and traces; use LangGraph for agent logic and a workflow engine like Temporal for long-running reliability. The two solve different problems, and a custom loop is right only when the agent is short-lived and the team already owns the retry and tracing plumbing.
AI handles specification drafts, scaffolding, tests and boilerplate; senior engineers still own architecture, security and judgement. The gain is real and comes from removing the mechanical parts of delivery, not from replacing the people who decide what to build, how to structure it and whether it is safe to ship.
An API façade turns a monolith into a set of independent replacement choices and lets AI and partners integrate immediately. It sits in front of the legacy code, exposes clean, versioned endpoints for the capabilities the business needs, and hides the database, screens and quirks behind a contract you control.
Add AI as a thin service beside your backend that calls your APIs with the user's token; no data model changes required. The AI layer owns prompts, retrieval, routing and evals; your product keeps owning data, permissions and business rules. Here is the architecture, what it touches, what it leaves alone, and the cost.
An AI agent is software that plans, uses tools, takes actions in your systems and escalates to people when it should, unlike a chatbot that only answers questions. This guide explains how agents work, what they can do today, what they cost and how to start safely.
Yes, an AI discovery sprint can be fully DPDP-compliant, because a well-run sprint needs almost no personal data. Work from redacted or synthetic samples, keep processing inside your own cloud account, set a retention date before day one, and log every access.
Basic RAG demos well and fails in production for six specific reasons: poor parsing, one-size chunking, vector-only search, stale indexes, missing permissions and no way to measure relevance. Each has an engineering fix; here they are.
AI proof of concept development runs in five moves: pick one workflow with a measurable outcome, assemble a labelled evaluation set, build the thinnest system that could pass it, score honestly, then decide. Eazyware runs this as a three-week ProofRun from $6,250 or ₹4 lakh.
AI proof of concept projects fail for five reasons, and rarely because the model was not good enough: no scoreboard, scope built around a technology rather than a workflow, curated data, unmeasured cost per case, and no owner with authority to act on the result.