Six programs. One mission.

Make your engineering organisation AI-native.

Every program is agnostic to tooling — no certification, no product walkthrough, no prompt library. What is taught are the durable properties: how models fail regardless of vendor, where deterministic code must own the answer, and how to specify, verify, observe and govern.

AI-Native Engineering: From Prompt to Production

A hands-on program that teaches engineering teams to build with AI — and to build AI that survives production.

Format Two consecutive days, 11 contact hours
Audience Software engineers, senior and staff engineers, tech leads, architects, engineering managers
Cohort size 20–25 participants, in person
Prerequisite Fluent in one mainstream language, able to read Python. No ML or AI background required.

Developers report significant productivity gains with AI. Delivery metrics rarely show them. Code is produced in minutes, then queues — for review, for testing, for someone prepared to trust it.

And a second, harder problem arrives when AI enters the product itself. An AI feature can return a wrong answer while every operational dashboard remains healthy — latency normal, traces clean, the customer misinformed.

Neither problem yields to a better model or a cleverer prompt.

Day 1

Build with AI

Module 1 · Rewire

Specifications that govern generation. Engineered context. The five failure modes behind every AI defect.

Module 2 · Harden

Review tiered by blast radius. Mutation-tested suites. CI gates generated code cannot bypass.

Lab 1 · Build With AI

Failing tests first, the smallest reviewable diff, peer review for intent drift — in a codebase nobody has seen.

90 minutes

Day 2

Build AI that survives production

Module 3 · Build

Evals built from observed failures. Tool contracts agents cannot misread. Least-privilege containment.

Module 4 · Run & Scale

SLOs on answer quality. Traces that replay any bad output. Migration gated on evidence.

Lab 2 · Build AI That Survives Production

Harden a live agent, strip its permissions, build its eval set, trace a request, work an incident.

90 minutes

The lab

Participants work inside the OpenTelemetry Astronomy Shop — a live 20-service microservice estate with a real AI agent, real traces, and injectable failures.

Runs entirely on participants' laptops, in local containers. No client source code, systems, data or network access needed at any point.

What participants take away

13 artifacts, built by hand — not handed out.

Codebase context brief · Behaviour contract with acceptance criteria · Deterministic tests including money boundaries · Working implementation and tool registration · Peer review notes · Hardened tool contract and agent policy · Read-only permission allowlist · Seven-case eval set with adversarial case · Failure taxonomy · End-to-end trace analysis · Incident diagnosis and regression test · Safe model rollout proposal · Draft rules file for their own repository

Measuring outcomes

Before Baseline DORA four keys, plus review time, PR size, and unreviewed merge rate.
Immediately after Capability self-assessment and each team's 24-week roadmap.
At 12 weeks Re-measure the same baseline. Throughput minus rework is the number that matters.
Ongoing For teams shipping AI features: quality SLOs and four operational signals, in the same dashboard as availability.
86%
Rated Good or Excellent
4.4/5
Content clarity
93%
Rated depth "just right"

Several teams adopted AI-native engineering practices as a direct result.

Participant feedback, Intuit Bengaluru — August 2026

Also available — foundations

AI-Native Leadership Briefing

What AI changes about roadmaps, team topology, hiring and governance — a candid, CXO-ready session on leading engineering in the AI era.

60–90 minutes · VPs of Engineering, CTOs, Chief Architects, Engineering Directors

Real-Time ML & Intelligent Decisioning

Streaming feature pipelines, online inference and risk decisioning under strict latency budgets — patterns proven on Visa-scale payment and fraud flows.

1–2 days · Engineers and architects building real-time ML systems

Distributed Systems Masterclass

Microservices, event-driven architecture and domain-driven design. Resilience engineering that cut RTO from 8 hours to under 1 — for legacy estates.

1–2 days · Engineers modernising legacy systems

Cloud-Native Platform Engineering

Kubernetes at enterprise scale, multicloud control planes, golden paths, observability and SRE — from building Nutanix Cloud Manager.

1–2 days · Platform and infrastructure engineers

Start with a 60-minute briefing.

The case, the evidence, and a live demonstration of an AI answer that is confidently wrong while every trace looks healthy. An honest read on where your teams actually are.

No cost, no procurement, no commitment.

Book a briefing

deepak@deepakbysani.com · +91 96633 31631