Practical notes from the engineers who build agents, copilots and AI products.
Measure an AI customer service agent on resolution rate, seven-day reopen rate, cost per resolved conversation, escalation quality and CSAT on agent-handled contacts. Deflection is not a metric: it counts conversations that ended, not problems solved. Track the five together, because each can be gamed alone.
An AI customer service agent is not compliant or non-compliant by nature; your deployment of it is. Under the DPDP Act, 2023 you need a lawful basis and notice for the conversation data, redaction before it leaves your boundary, a retention clock, erasure on request, and processor terms with every model vendor.
You implement LLM application development in five phases: choose one workflow with a measurable outcome, build the evaluation set before the prompt, design retrieval and tools around your real data, ship behind a flag to a small cohort, then instrument quality and cost. Sequence matters more than model choice.
LLM application development differs less in architecture than in constraints. A startup ships one workflow fast and accepts manual fallbacks; an enterprise ships behind access control and audit, spending most of its budget on integration and evidence rather than the model layer. Here is where the builds diverge.
LLM application development projects fail for five recurring reasons: no evaluation set, retrieval over content nobody maintains, a demo promoted to production, actions without approval gates, and no named owner after handover. Each has one decision that prevents it, and all five are visible well before launch.
LLM application development is not compliant by default. India's DPDP Act governs the personal data inside the application: lawful consent, a stated purpose, bounded retention, access controls that survive retrieval, and evidence you can produce on demand. Here is the clause-by-clause checklist.
Measure LLM application development on four layers: output quality against a fixed golden set, retrieval quality, production behaviour, and the business outcome. Demo impressions are not evidence. Build the evaluation suite before the feature, gate every release on it, and track cost per task next to accuracy.
A text-to-SQL accuracy number only means something against a defined set of your users' questions, run on your schema, with correctness judged on the returned result and reported by question type. Here is how to measure it and what moves it.
AI copilot development runs in five stages: pick three jobs users already do in your product, expose them as scoped tools over your existing API, ground the copilot in your own data, build the evaluation suite, then launch behind a flag. The architecture decisions matter more than the prompts.