Why 90% of Teams Fail at AI-Assisted Development
Your team adopted GitHub Copilot or Cursor expecting productivity gains.
Instead, you're spending more time reviewing AI code than writing it yourself.
After 2+ years of daily production AI development — not experimenting, shipping — I've figured out what actually works when everyone else is still guessing.
The Review Trap
This is what happens on most teams that adopt AI coding tools
"I could have written this myself faster."
By the third review cycle, the developer realizes the AI is creating more work, not less. They're right.
Net result: 0-10% productivity gain, maximum frustration
Context Window Collapse
AI agents have limited context windows. As sessions progress, context fills and gets compacted. Agents "forget" earlier instructions, architectural decisions, quality standards.
No Natural TDD
AI agents don't naturally follow Test-Driven Development. They want to write implementation first, leading to broken tests, incorrect assumptions, and missed edge cases.
Unconstrained Chaos
Without explicit constraints, agents duplicate code, violate architecture, lose track of complexity. Every output requires extensive human review to catch the damage.
Constrained Autonomy
The counterintuitive insight: more constraints = more speed
This is NOT about typing faster.
It's about delegating entire features to AI agents and getting it right the first time.
Understanding LLMs
Why the same prompt produces mediocre or excellent results. How to "activate" high-quality reasoning patterns that make agents actually useful.
Sub-Agent Architecture
Extends productive sessions from ~12 steps to 120+ steps. Planning agent orchestrates; task agents reset after each step. Context doesn't degrade.
Quality Gates
95%+ test coverage (TDD-enforced). Zero-tolerance quality gates. Single command runs all checks. Agent must achieve 0 errors — no exceptions.
Workflow Modes
TDD for implementation, plus specialized modes for different task types. Agent autonomously selects the right process for each situation.
Parallel Workflows
When to run agents in parallel, when step-by-step. Each developer manages multiple features simultaneously. Run architecture changes overnight.
The Result
Delegate complete features, review once, ship. Focus on architecture and design, not boilerplate. Work on what matters while agents handle the rest.
Without Constrained Autonomy
With Constrained Autonomy
Advanced teams with 6-12 months of practice reach 200-600% improvement
Alex Fedorov
Founder & CTO, SCIQOS Consulting
2+ years of full-time AI-assisted development — not occasional use, daily production work. Shipping real software to real users.
Built and scaled AI-first engineering teams. Delivered large-scale FinTech applications using autonomous development workflows.
This isn't theory. It's a battle-tested methodology refined through thousands of hours of production development.
2+ Years Daily Production
Full-time AI-first development, not experimenting
AI-First Teams Built
Scaled engineering organizations with agentic workflows
Large-Scale FinTech Delivery
Production applications using autonomous workflows
What Engineers Say
Results from recent workshop participants
The AI workshop taught me how powerful the latest AI tools are. Alex shared his knowledge in a clear manner, teaching techniques and workflows to take the most out of AI tools. The hands-on exercises were crucial... I'm now a much more productive developer thanks to the skills I gained.
The focus was practical: helping developers like me actually use AI tools effectively in real development work. I learned to create AI agents specifically for architecting Infrastructure as Code — which was a game-changer... My AI tool usage skills have genuinely scaled up.
This was helpful not only to share knowledge and how we should approach AI assisted development, but also to train the team mindset on how to apply the tools... we discussed how to adapt our approach and processes and power it up with AI, without losing delivered value and quality.
It was nice seeing what everybody had built in so little time during the project days... My biggest takeaway was knowing what's out there, the possibilities of each tool and fine-tuning my use of AI for development.
Choose Your Format
Both formats include preparation, hands-on practice on your codebase, and follow-up
Workshop
2 days: 1 day theory + 1 day practice
Best for teams who want to understand the approach and get started quickly. Get the foundations in place and begin seeing results immediately.
Day 1: Foundations & Mental Models
- How LLMs work: priming for quality vs. mediocre output
- The spectrum: Assisted to Agentic to Autonomous
- Context management & sub-agent architecture
- Live demo: maximum capability in action
Day 2: Your Codebase
- Quality gate setup for your stack
- Workflow modes: TDD and beyond
- Hands-on feature implementation
- Q&A: address your specific challenges
Bootcamp
5 days: 1 day theory + 4 days practice
Best for teams who want deep hands-on experience and leave fully proficient. Each participant implements multiple real features with real-time coaching.
Day 1: Foundations & Mental Models
Same comprehensive foundation as Workshop Day 1
Days 2-3: Green Field Practice
- Build new features from scratch
- Progressive complexity: simple to advanced
- Real-time coaching on pitfalls
Days 4-5: Brownfield Practice
- Work on YOUR company's actual codebase
- Real features from your backlog
- Edge cases, debugging, advanced techniques
What's Included in Both Formats
Pre-Workshop Preparation
- Custom CLAUDE.md for your stack
- Sub-agent prompts for your workflows
- Hook scripts for quality enforcement
- Linting & static analysis configs
- Designed for existing codebases
During
- On-site or remote delivery
- Up to 12 participants
- Hands-on with your actual codebase
- Real features from your backlog
- Individual attention & coaching
After
- Follow-up session 2 weeks later
- Address questions that emerge
- Refine workflow based on experience
- Troubleshoot any blockers
Requirement: Claude Code
Subscriptions required separately
The workflow is built specifically on Claude Code. The key features — sub-agents, hooks, context management architecture — don't exist in other tools. This isn't preference; achieving this level of agentic automation requires these capabilities.
Claude Code is additive, not a replacement. Teams typically keep Cursor for reading code and quick inline edits.
Subscription Options (per developer/month)
Stop Reviewing. Start Delegating.
Get in touch to discuss which format fits your team.
We'll send a preparation questionnaire to customize the experience.
SCIQOS Consulting — Transforming how teams build software with AI