ByteCreators & Xavia Solutions join forces to unlock the full potential of Artificial Intelligence — from strategy to deployment, for businesses ready to lead.
AI-Assisted
→ AI-Driven
Moving beyond prompts
and assistants
What we'll cover
The shift
From AI-assisted to AI-driven
The bottleneck
Where time really goes
Live demo
One command → full pipeline
Mindset
Where NOT to use AI
AI concepts
LLM ≠ Agent ≠ System
Models + infra
Local, cloud, hybrid
Hackathon case
Contract-first, multi-agent
PR-Agent
Real numbers, real adoption
How to start
Playbook + 7 principles
You're already using AI
ChatGPT
Cursor / Copilot
Prompt patterns
What do you use AI for?
Scan & answer
CODE
Build This System
Not just UI — the full flow. You have 15 minutes.
📋 Requirements
Simple question with multiple options
Users can submit an answer
Responses are stored
Live results update (no refresh)
Shareable or accessible endpoint
🛠️ Use anything you already have
AI-Assisted vs AI-Driven
AI-Assisted
You ask → AI responds
- Reactive
- Manual trigger
- Tool-centric
- Step by step
- Check out bytecreators cloud
AI-Driven
Event → AI executes
- Proactive
- Event-triggered
- System-centric
- Fully automated Check out ByteAI
Most teams upgraded their tools
Very few upgraded their systems
— Abdul Waris
Event → Processing → Action
Event
Something happens
Processing
AI thinks + acts
Action
Real output delivered
What triggers the system?
PR opened
Automated review kicks in instantly
Git Workflow triggered
Context fetched and Review done automatically
PR Merged
Automatic Build and Deployment pipeline executes end-to-end
Check this out
Where is time spent?
AI improved coding
Most time is still
🔥 The Demo
Let me show you something real
15Run this command:
(show live or video)
Traditional Flow
Requirement
Dev
Review
Fix
Deploy
Our Flow
One command
System executes
Live output
🧠 Engineering Mindset
Knowing where NOT to use AI is the real skill
23Not everything should
be AI-driven
Good engineers know the difference
DB Access — Before
Use AI Connectors
You just dressed it up differently
26Your Approach
Bash script
One command
Full flow executed
One command → Full execution
(Replace with actual terminal screenshot)
28AI amplified
engineering
It didn't replace it
🏗️ Models & Infra
Local, cloud, hybrid — picking the right backbone
39Model Size Matters
✔ Speed ⚡
• Less capable
✔ Good balance
• Moderate
✔ Smartest 🧠
• Slower, costly
Context Window
How much data the model sees at once
Think of it as the model's working memory
41Local vs Cloud
Local
- ✔Full control
- ✔Private data
- ✔No API costs
Cloud
- ✔More powerful
- ✔Latest models
- ✔Easy to scale
Local + Cloud
Local for private data · Cloud for heavy lifting
43VPS Setup Stack
VPS (Contabo)
Docker containers
Local models (Ollama)
Automation scripts
🛠️ Tooling Strategy
My stack, free vs paid, and warnings
45My Stack
OpenCode
Long, complex tasks
Ollama / Qwen
Precise, local execution
Claude
Complex reasoning
Free vs Paid AI
Free
- Junior assistant
- Slower output
- Less capable
Paid
- Senior engineer
- Faster output
- Better quality
You don't save money
with free AI
You spend it
in time
Warning
AI builds fast
your architecture
You have to care about it.
49CTO Thinking
Example scenario:
AI uses external backend
→ You lose control of your data
As engineers, we don't just
use AI
We control
where it operates
🏆 Beyond Autocomplete
How we won a hackathon with AI-Native, Contract-First development
52🔧 Teaching the AI how we work
Before the clock started, we did one thing most teams skipped:
We onboarded the AI like a new engineer
Conventions, decisions, gotchas — written down so the AI would respect them under pressure.
📏 What are "Rules" and "Memory"?
Rules = Permanent Instructions
Hard constraints the AI must always follow.
- → Always use TypeScript strict mode
- → Never commit without tests
- → Use repository pattern for data
Memory = Background Context
Things the AI should know but can override with judgment.
- → We use Postgres for everything
- → Frontend is Next.js + Tailwind
- → Auth lives in /lib/auth
📜 The Single Source of Truth
One contract that everyone follows
55🔗 What is the "Source of Truth"?
A single document — usually a Markdown spec — that defines:
- → Domain entities & their fields
- → Business rules in plain English
- → API contracts (input / output)
- → Acceptance scenarios (Gherkin)
- → Edge cases & error paths
- → Non-goals (what NOT to build)
Two Approaches. One Winner.
🤠 Cowboy Coding
- Prompt → code → fix → prompt → code...
- AI hallucinates field names
- Tests written after the fact (or not)
- Endless rework when reqs shift
- Each agent re-derives the spec
📜 Contract-First
- Spec → tests → code → ship
- AI references the same contract
- Tests come from scenarios directly
- Refactor by editing the contract
- One source of truth across all agents
🚢 The Mindset Shift
"I'm the one at the sail, I'm the master of my sea"
58🎭 The Agents We Used
Seven specialized AI roles, each with a single responsibility:
Architect
System design, boundaries, ADRs
Specifier
Writes the contract & scenarios
QA Engineer
Edge cases, error paths
Test Writer
Failing tests from scenarios
Builder
Code that makes tests pass
Auditor
Reviews code for drift
DevOps
CI/CD setup, deployment, infra-as-code
🧠 Why separate roles?
One agent doing everything
- ✕ Mixes design with implementation
- ✕ Loses focus on long prompts
- ✕ Skips edge cases under pressure
- ✕ Hard to audit decisions
Specialized agents
- ✓ Each has narrow context
- ✓ Output is the next agent's input
- ✓ Clear handoff = clear audit trail
- ✓ Mistakes are easier to spot
🔄 How agents passed work to each other
Architect
ADR.md
Specifier
contract.md
QA
scenarios.feature
Test Writer
*.test.ts
Builder
src/
Auditor
audit.md
Each output became the next agent's input.
61🔴🟢 Write → Test → Build Loop
How we verified the AI's output at every step
62🥒 Scenarios: plain-English behavior
Anyone on the team can read this. The AI translates it to a real test.
64🔴🟢 Red → Green: the power of this loop
🔴 Red
Write the test FIRST.
It fails because the code doesn't exist yet.
This proves the test actually validates something.
🟢 Green
Now ask the AI to make it pass.
The smallest change that turns red → green.
No scope creep. No "while you're at it..."
🧩 Domain-Driven Design — in plain terms
Break a big problem into bounded contexts — each owning its own data and language.
- → Each domain has its own model
- → Cross-domain talk via events
- → Tests stay focused
🗺️ Our Business Domains
- → Trip — route, ETA, completion
- → Driver — profile, status, history
- → Routing — geo, traffic, quality score
- → Pricing — surge, fare, discounts
🔬 Anatomy of a winning prompt
What worked first try ✓
- → Reference the contract path
- → Single, narrow task
- → Existing test as the success criterion
- → State the constraints (NO new deps)
What needed retry ✕
- → "Build the whole feature" (too big)
- → Vague success criterion
- → Conflicting context across messages
- → Missing edge cases in the spec
📊 Prompt Success Rate
The contract carried the day — most prompts didn't need rewording.
68⏱️ 20 Commits in 7 Hours
The commit history tells the story
69📈 The Numbers
🔑 The most surprising stat
40%
of our time produced zero lines of code
Spec-writing, scenario-modeling, agent setup — all "non-coding" work.
🔄 Design-Heavy Is a Feature, Not a Bug
Hours 1–3
No code committed.
- → ADR + contract
- → Scenarios
- → Agent prompts
Hours 4–7
20 commits — all small, all green.
- → Test → code → audit per feature
- → No rework cycles
- → Consistent style throughout
💡 The 7 Principles
Onboard the AI like a new engineer
Contract first, code second
One source of truth across all agents
Specialize agents — one role each
Red → Green always
Audit what the AI ships
Design-heavy is a feature — not a bug
🎯 What this hackathon proved
A small team, with a clear contract and specialized agents, can ship in hours what traditional teams ship in weeks — without sacrificing quality.
🔍 Automated AI Code Review
From manual bottleneck to AI-driven on every PR
76Manual review is a bottleneck
Senior engineer time
20–30 min per PR review. At scale this compounds across every microservice.
Consistency gaps
100+ engineers, multiple teams. Standards vary. What's caught here gets missed there.
Existing tools fall short
Codex hits limits after 1–2 reviews on the $8 plan. Copilot free tier silently skips PRs.
PR-Agent — open source AI reviewer
Runs as a GitHub Action or Bitbucket Pipeline
PR opened
or new commit
Pipeline triggers
GH Actions / BB
Diff sent to GPT
changed lines only
Bot comments
inline on exact line
What it catches on every PR:
any usageWhat the review looks like
PR Reviewer Guide 🔍
Estimated effort to review: 4 ●●●●○
No security concerns identified
▼ Missing Validation
Course model has no validation on fillable attributes — invalid data can be saved.
▼ Missing Null Checks
enrollments() and students() don't check if relationships exist before accessing.
▼ SQL Injection Risk
Raw user input in query — use bindings.
Available Commands
/review— Re-run full review/improve— Get committable fixes/ask ...— Ask about the diff/describe— Auto-generate PR title
Real Numbers — March 2026
Actual usage from personal repos (GitHub + Bitbucket combined)
Monthly budget: $0.06 used of $5.00 limit
How it works — technically
GitHub Setup
.github/workflows/pr-agent.yml uses: Codium-ai/pr-agent@main
Triggers:
- • PR opened / reopened
- • New commit pushed (synchronize)
- •
/review /improve /askin comments
Runs on: GitHub workflows / self-hosted runner
Cost: Only OpenAI token usage
Bitbucket Setup
bitbucket-pipelines.yml docker run codiumai/pr-agent:latest
Triggers:
- • PR opened / reopened
- • New commit pushed (synchronize)
- •
/review /improve /askin comments
Runs on: Bitbucket cloud / Jenkins / self-hosted
Cost: Only OpenAI token usage
Setup is ready.
Just need the go-ahead.
Manual review was the cost
of doing business
Now it's
$0.06/month
🚀 How to Start
A repeatable playbook for any team
85📖 The step-by-step playbook
Pick one flow in your team — not a whole pipeline
Write the contract — one Markdown doc, source of truth
Onboard the AI — rules, memory, conventions
Specialize agents — architect, specifier, builder, auditor
Run the loop — define → test → build → verify
Audit every step — AI builds fast, you keep it honest
Measure & expand — token cost, time saved, quality
Start Small
Pick ONE flow in your team
Not a whole pipeline. Not a new platform. Just one trigger. One output.
Define your flow
Event
What triggers it?
AI
What does AI do?
Output
What's delivered?
Pick one flow from your team
→ What event exists in your workflow?
→ What could AI do with that event?
→ What output would save you time?
Share your answer with the group 👥
89The Journey
AI-Assisted
You ask → AI responds
Reactive · Manual · Tool-centric
AI-Driven
Event → AI executes
Proactive · Automated · System-centric
AI won't replace
engineers
Engineers with systems
will win
Let's build one
real flow together
I'll help your team:
- 1Pick the right trigger
- 2Design the pipeline
- 3Ship a working AI-driven flow