Slide 1 of 60: AI-Native Code Generation
AI-native development

AI-Native Code Generation

Amarnath Prasad

Founder of Cralo AI Unacademy · JioHotstar · Memfold AI

Amarnath Prasad
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AI-native development

AI-Native Code Generation

Amarnath Prasad

Founder of Cralo AI Unacademy · JioHotstar · Memfold AI

Amarnath Prasad
AI-native developmentThe new engineer

You hire a brilliant engineer

Now imagine their first day.

AI-native developmentThe new engineer

No access. No feedback.

No repository access

No CI. No logs. No shell.

AI-native developmentThe new engineer

No context. No verification.

No docs, JIRA, or Slack history

No way to run the tests

AI-native developmentThe new engineer

You do the handoff over calls

That’s how most teams use AI agents today.

AI-native developmentThe thesis

Prompting isn’t enough. Engineer the system.

AI-native developmentThe roadmap

The talk, in five beats

  1. 01
    What AI-native development actually is
  2. 02
    Prompt → context → loop engineering
  3. 03
    A live walkthrough of an AI-native product
  4. 04
    Large codebases, then software factories
  5. 05
    Five principles to take home

AI-native development

The familiar lifecycle

AI-native developmentThe familiar lifecycle

The software development lifecycle

PRD

Architecture

Modules

Tasks

Code

Review

QA

Ship

Monitor

AI-native development

The AI-native lifecycle

AI-native developmentThe AI-native lifecycle

Same flow. New execution.

PRD

Agent drafts spec.md. You correct and approve.

Architecture

Agent suggests an approach. You decide.

AI-native developmentThe AI-native lifecycle

Plan once. Build in parallel.

Tasks

Agent writes plan.md.

Build

Agents in git worktrees. One branch per task.

AI-native developmentThe AI-native lifecycle

Verify first. Review the PR.

Review

Agents verify first. You review the PR.

Ship

Automated. Agents watch the signals.

AI-native developmentOwnership

What stays human

Humans

Stack. Architecture. Product. Merge. Release.

Agents

Draft. Build. Verify. Iterate until checks pass.

Humans: decisions and gates. Agents: execution.

AI-native developmentThree eras

From prompts to autonomous loops

2022–23
01

Prompt

Craft the ask

2024–25
02

Context

Give it the knowledge

2026–
03

Loop

Act, verify, learn

Context Engineering

Context engineering

Give the agent the right information.

Context Engineering

Performance = intelligence × context

Right context, right time — not a dump.

Context Engineering

Connect everything

Docs
JIRA
Slack
Incidents
Design
Code
AgentSearch → retrieve

Retrieve just-in-time. Connected ≠ pre-loaded.

Context Engineering

Let the agent retrieve it

jira issue view PROJ-123
 
rg "invoice" packages/billing

Search at runtime. Pull only what the task needs.

Context EngineeringConnecting sources

MCP: structured tool servers

  1. 01
    Register in Cursor / Claude Code
  2. 02
    Jira, Slack, docs as typed tools
  3. 03
    Schemas the agent discovers

Context Engineering

Keep context in the repo

Context EngineeringKeep context in the repo

AGENTS.md

# how this repo works
pnpm test --filter payments
 
# never
# edit generated files in gen/
# raw SQL — use make db-migrate
 
# deploy
main → prod · feature/* → preview

Always loaded. Keep it small.

Context EngineeringSkills

Skills load on demand

AGENTS.md is always on. Skills are not.

  1. 01
    Point to the source of truth

    Don’t duplicate docs.

  2. 02
    Be opinionated about workflows
  3. 03
    Security rules stay always-loaded
Context EngineeringKeep context in the repo

Keep context in the repo

my-repo/
.skills/On demand
deploy/How we ship
AGENTS.mdShared rules
packages/
payments/
AGENTS.mdPayments rules
billing/
AGENTS.mdBilling rules

Small root file. Skills on demand. Package-specific detail.

Loop Engineering

Loop engineering

Give the agent a way to act, check, and improve.

Loop Engineering

The whole idea, in one line

while :; do
  cat PROMPT.md | claude-code
done

Fresh context each iteration. Progress lives in the repo.

Loop Engineering

Run until the work is done

Re-read spec

Work one task

Check tasks.md

Observe · repeat

Exit when fully checked off. Failing checks keep the loop alive.

Loop Engineering

Close the feedback loop

CI fails

Read logs

Hypothesize

Fix

Re-run

A failed check becomes the next action.

Loop Engineering

Verification

Loop EngineeringVerification

Verification is the exit condition

Test suite + CI

Exit code 0

Playwright

Screenshot → compare → fix

Staging smoke

Real deploy, end to end

Loop EngineeringVerification

The verification pyramid

Human judgment
Adversarial review
Skills · lint rules
Tests · types · linters

Loop Engineering

Runtime

Loop EngineeringRuntime

Remote Docker container

Isolated. Reproducible. Disposable.

Loop EngineeringRuntime

Give the run only what it needs

  1. 01
    Scoped tokens from your vault
  2. 02
    Only the connectors this task needs
  3. 03
    Staging-only deploy credentials

Jira · Slack · GitHub · database

Loop EngineeringRuntime

One disposable run

docker run --rm \
  -e JIRA_API_TOKEN -e GITHUB_TOKEN \
  agent-loop:latest

Inject scoped credentials. Keep secrets out of logs.

Loop EngineeringRuntime

Set boundaries before the run

  1. 01
    Restrict commands, paths, and network access
  2. 02
    Set time, retry, and cost limits
  3. 03
    Stop and escalate when progress stalls
  4. 04
    Tag agent PRs. Keep human merge approval.

Loop Engineering

Event triggers

Loop EngineeringEvent triggers

Machines initiate. Humans approve.

Event

GitHub · Jira Sentry · Slack

Agent loop

Plan · build · verify

Human at merge

Reviews the PR

Loop Engineering

Make the next loop better

Loop EngineeringCompound knowledge

Teach the system, not the session

Ship a task

Capture the lesson

Encode it

Save the lesson in AGENTS.md or a skill.

Loop EngineeringCompound knowledge

Turn lessons into checks

  1. 01
    eslint-plugin / no-raw-sql
  2. 02
    Pre-commit hook
  3. 03
    Codemod
  4. 04
    CI gate

Repeated mistakes become repeatable checks.

Loop Engineering

Design loops that prompt your agents

“You shouldn’t be prompting coding agents anymore.” — Peter Steinberger, creator of OpenClaw

A live example

A live example

Large-codebase playbook

Large-codebase playbook

The same loop needs a repo it can navigate.

Large-codebase playbook

Make the repo answer questions

  1. 01
    Fast whole-repo search

    Grep at this scale is infrastructure.

  2. 02
    Make the repo structured

    Clear modules. Obvious boundaries.

  3. 03
    Rules beside the code

    Local AGENTS.md, not a dump.

Navigability is your job. Retrieval is the agent’s.

Large-codebase playbook

Keep the codebase maintainable

  1. 01
    Characterization tests before refactoring
  2. 02
    Human gates on architecture and merge

Agents fail silently on cross-cutting change.

Large-codebase playbook

Make “done” mean something

  1. 01
    Tests + perf budgets in the definition of done
  2. 02
    Target 2–3× faster, safely — not 10× without review

Time to an accepted change — not time to generate code.

Software factories

Software factories

Extend the loop until the operating process is the product.

Software factoriesIndustry adoption

Most teams: inline AI + agent mode

  1. 01
    Accept / reject every line
  2. 02
    Review plans and diffs
  3. 03
    A human still sits in every loop
Software factoriesIndustry adoption

The frontier: triggered, autonomous

70%+

PRs involve agents

Uber

5–30h

With nobody watching

Anthropic

Claude Code and the CLIs

Software factories

Extend the loop → the factory

Signals → triage

Plan → build

Verify → ship

Observe · repeat

Monitor → production feedback → back into the queue

Take home

Take home

Five principles to carry into Monday.

Take homeFive principles

Don’t assume capabilities

Your agent is ultra capable. Don’t underestimate it.

Take homeFive principles

Give as much autonomy as possible

Don’t sit in every loop.

Take homeFive principles

Guardrailed access to real tools

Logs, CI, shell — in a sandbox.

Take homeFive principles

Connect everything. Retrieve just-in-time.

Right context, right time.

Take homeFive principles

Let the agent test its own work

Run tests. Use the browser. Check it the way a human would.

Take homeFive principles

Five principles to take home

  1. 01
    Don’t underestimate the agent
  2. 02
    Give it autonomy
  3. 03
    Provide guardrailed access to tools
  4. 04
    Connect context. Retrieve when needed.
  5. 05
    Let it test its own work
Take homeKeep up

Be in the room where it happens

Follow the practitioners and the labs

Practitioners
@dexhorthy profile@dexhorthy
@hraness profile@hraness
@addyosmani profile@addyosmani
@steipete profile@steipete
Labs & products
@AnthropicAI profile@AnthropicAI
@OpenAI profile@OpenAI
@cursor_ai profile@cursor_ai
Take homeThank you

Questions?

Engineer the system

@amrnth0 amrnth.com