Malay Parekh

A production voice AI agent runs three integrated layers: ASR, LLM, and TTS. Each adds latency. Total end-to-end response time in production typically runs 1.5 to 3 seconds.

Model Context Protocol (MCP) is Anthropic's open standard for agent-tool integrations. Think USB-C for AI: one standard, many tools, far less custom code.

Traditional CI/CD assumes deterministic code. AI agents do not. You need a separate evaluation layer, prompt and model versioning, behavioural monitoring, and canary rollouts that catch regressions.

Adding LLM features to Flutter apps is easy to prototype but hard to scale. Latency, cost control, and graceful offline fallbacks are the three production challenges that matter.

Xano wins for enterprise deployments, MVPs that need to scale, internal tools, and regulated workloads. Supabase wins for SQL-native teams with pgvector. Custom backends only when truly specialised.

Most AI projects fail after the demo, not during it. These 7 questions reveal whether a partner can actually take a working prototype into production and keep it healthy.

AI agent development in 2026 ranges from $8K for a simple PoC to $150K+ for a production multi-agent system. The primary cost driver is integration complexity, not the AI model itself.

Google Workspace wins for browser-native teams that want deeper AI collaboration. Microsoft 365 wins for Windows-heavy environments with Office desktop dependency.

AI in software engineering is no longer just about fixing syntax or providing simple code. The discourse for engineering teams, CTOs, and product leaders has changed from "How do we write code...