What ADI Actually Solves: Real Tasks, Real Results

Let's be honest: not every development task requires deep human creativity. A lot of software work is mechanical—fix the TypeScript error, update the API endpoint, clean up that deprecated import. Important work, but not work that requires your unique problem-solving skills.

That's ADI's sweet spot. Here's what it actually handles in production, with real examples.

Category 1: Bug Fixes

The bread and butter of autonomous AI development. ADI excels at bugs with clear error messages and reproducible conditions.

Real Example: TypeScript Type Errors

Task: "Fix TypeScript errors in admin.ts preventing build"

What ADI Did:

Analyzed compilation errors from build output
Identified missing type annotations in admin handler functions
Added proper types following existing patterns in codebase
Verified build passes after changes

Time: 4 minutes Cost: $0.23 Human Review Time: 2 minutes to approve MR

Why It Works: Error messages are precise, fix patterns are well-documented in TypeScript ecosystem, verification is binary (builds or doesn't).

Real Example: Pipeline Execution Refactoring

Task: "Fix error handling in pipeline executor causing silent failures"

What ADI Did:

Located error swallowing in try-catch blocks
Added structured error logging with context
Ensured errors propagate to pipeline status API
Added tests for error scenarios

Result: Silent failures eliminated, error visibility improved

Why It Works: The codebase had examples of proper error handling elsewhere. ADI pattern-matched and applied consistently.

Category 2: Simple Features

Features with well-defined scope and clear acceptance criteria. Think CRUD operations, API endpoints, configuration additions.

Real Example: AWS S3 Integration

Task: "Implement AWS S3 bucket integration for file uploads"

What ADI Did:

Added @aws-sdk/client-s3 to dependencies
Created storage service following existing file handling patterns
Implemented upload, download, and delete methods
Added error handling for network failures and rate limits
Wrote integration tests matching project testing conventions
Updated environment configuration for AWS credentials

Time: 8 minutes Cost: $0.47 Lines Changed: ~200 (new service + tests)

Why It Works: AWS SDK documentation is excellent, pattern already existed for other storage providers, integration tests provided clear success criteria.

Real Example: API Usage Tracking

Task: "Add API usage tracking and cost monitoring system"

What ADI Did:

Analyzed existing database schema
Added new tables for usage tracking
Implemented tracking middleware in API layer
Created cost calculation utilities
Added admin dashboard queries
Updated API client to report usage

Time: 12 minutes Cost: $0.68 Lines Changed: ~350

Why It Works: Task had clear data model requirements, existing middleware patterns to follow, straightforward business logic.

Category 3: Technical Debt Cleanup

The "I'll fix this later" pile that never gets fixed. ADI loves these.

The "Хвостов" (Tail-End Issues)

In Ukrainian/Russian dev culture, "хвостов" refers to those lingering loose ends—unused imports, outdated dependencies, inconsistent naming, missing documentation.

Real Examples ADI Has Handled:

Unused Imports Cleanup:

Scanned entire codebase for unused imports
Removed 200+ unused import statements
Verified builds and tests still pass
Time saved: ~2 hours of manual work

Deprecated API Migration:

Identified deprecated GitLab API endpoints
Migrated to current API versions
Updated authentication flow
Updated tests for new API responses

Code Comment Verification:

Added missing JSDoc comments to public functions
Ensured comment quality meets project standards
Verified comments accurately describe behavior

Dependency Updates:

Updated minor version bumps for dependencies
Ran full test suite to verify compatibility
Updated lock files and CI cache

Why Technical Debt Works Well:

Tasks are well-scoped and repetitive
Success criteria are clear (builds pass, tests pass)
Patterns are usually obvious
Low risk of introducing bugs

Category 4: Configuration and Infrastructure

Infrastructure-as-code changes, CI/CD updates, environment configuration.

Real Example: GitLab CI Template Updates

Task: "Split monolithic CI config into separate implementation and evaluation pipelines"

What ADI Did:

Created .gitlab-ci-claude.yml for implementation pipeline
Created .gitlab-ci-evaluation.yml for evaluation pipeline
Updated worker orchestration to trigger appropriate pipeline
Ensured environment variables passed correctly
Verified both pipelines work independently

Why It Works: CI configuration is declarative, outcomes are testable (pipeline runs or doesn't), documentation is comprehensive.

What ADI Doesn't Solve (Yet)

Let's be realistic about boundaries:

❌ Complex Architecture Decisions

"Should we use microservices or a monolith?" — Requires business context and long-term vision ADI doesn't have.

❌ UI/UX Design

"Make the dashboard more intuitive" — Requires visual judgment and user empathy. AI can implement designs, but not create them from scratch.

❌ Novel Algorithm Development

"Optimize this recommendation engine" — Requires deep domain expertise and creativity beyond pattern matching.

❌ Ambiguous Requirements

"Add some social features" — What features? For which users? What's the goal? ADI needs specificity.

❌ Manual QA Tasks

"Test the checkout flow and see if it feels right" — Requires human interaction and subjective judgment.

The 60% Rule

Currently, 60% of tasks evaluated by ADI pass the "can implement" threshold. The other 40% get rejected because:

25% - Unclear or ambiguous requirements
10% - Missing dependencies or access
5% - Require human judgment or design decisions

That 60% success rate might not sound impressive, but consider: those are tasks that would have consumed developer time. Now they don't.

Real Cost Savings

Let's do the math on a typical sprint:

Before ADI:

20 tasks in backlog
12 are "AI-solvable" (bug fixes, simple features, tech debt)
Average dev time: 45 minutes per task
Total time: 9 hours
Cost at $80/hr: $720

With ADI:

Same 20 tasks in backlog
ADI evaluates all 20, implements 12
Average ADI time: 6 minutes per task
Average AI cost: $0.35 per task
Total time: 1.2 hours
Total cost: $4.20
Savings: $715.80 and 7.8 developer hours

Those 7.8 hours? Now spent on architecture, design, code review—work that actually needs human intelligence.

Task Writing Best Practices

Want ADI to succeed? Write tasks like you're briefing a competent junior developer:

✅ Good Task: "Add /api/tasks/:id/archive endpoint. Should set archived_at timestamp in database and return 200 with updated task object. Include test coverage."

❌ Bad Task: "The task system needs better organization."

✅ Good Task: "Fix TypeScript errors in src/admin.ts preventing build. Errors are in lines 45-67, missing type annotations on handler functions."

❌ Bad Task: "Code isn't compiling."

Specificity, context, and clear success criteria are your friends.

Real Developer Reactions

"I gave ADI a cleanup task I'd been avoiding for weeks—removing unused imports across 50+ files. Came back 10 minutes later to a perfect MR. Genuinely magical." — Frontend dev, Series A startup

"Bug fix tasks that used to take 30-45 minutes now just... happen. I review the MR, merge, done. It's changed how I prioritize work." — Tech lead, fintech company

"The evaluation phase is underrated. Knowing a task is 'AI-solvable' before wasting time on it is huge for sprint planning." — Engineering manager, SaaS platform

What's Next for ADI Capabilities

We're actively expanding what ADI can handle:

Visual regression testing - Automated screenshot comparison for UI changes
Database migrations - Schema changes with rollback safety
API contract testing - Ensure breaking changes are detected
Cross-repository changes - Features spanning multiple services

The 60% success rate today becomes 75% in six months.

Ready to reclaim those 7.8 hours per sprint? Try ADI with $100 free credit and see what it solves in your codebase.