Stop Doing the Parts of Your Job a Machine Should Handle
How developers hand off bug fixes, code review, docs and test coverage to an autonomous AI agent overnight — what works, what it costs, what it cannot do.
Let's be honest about what developers actually spend time on.
Building features? Yes. That's the good part. The flow state. The reason you got into this.
But between the features: reviewing PRs that are 90% boilerplate. Debugging edge cases at 11 PM. Writing documentation that will be outdated by next sprint. Explaining to the junior dev why their SQL query will bring down production.
These aren't the parts of the job anyone loves. They're the tax you pay to do the work that matters.
Burrak AI was built by developers who got tired of paying that tax. Here's how to offload the grunt work to agents — and which kind of agent does which part.
Two kinds of agent, and the difference matters for code. A remote agent runs in Burrak's cloud around the clock. It reads your repositories through the GitHub connector — code, issues, pull requests, history — but it has no shell, so it cannot check out a branch, run your tests or push a commit. A local agent runs in the desktop app on your own machine. Give it shell access (in Tools, set the execution policy to Allow, or Require approval to confirm each command) and it can work in your checkout: edit files, run the test suite, commit, and open a PR with your own git credentials. The sections below say which one each job needs.
Bug Fixes: Describe It at 10 PM, Merge It at 8 AM
The #eng-alerts channel lights up. Checkout is crashing when users apply discount codes with special characters. You look at the stack trace. You know it's probably a regex issue. You also know it's 10 PM and you have a life.
The old way: Sigh. Open VS Code. Reproduce. Trace. Find the unescaped regex. Fix. Write tests. Open PR. Go to bed at 1 AM. Wake up tired. Merge after review. Total: 3 hours of your evening, gone.
With Burrak AI:
"Bug in checkout-service: discount codes with special characters like 'SAVE-20%' cause 500 errors. Find the root cause, fix it on a new branch, add tests, run the suite, and open a PR."
Give that to a local agent with shell access and leave the machine on. It reads the checkout service, traces the discount validation path, finds the unescaped regex in discount_validator.py, writes the fix, adds tests for special characters, empty strings and max-length codes, runs your suite, and opens a PR describing the root cause.
No machine left on? Give the first half — "find the root cause and propose the fix as a patch" — to a remote agent. It reads the code through the GitHub connector and has the diagnosis and a proposed diff waiting in the morning; you apply it and run the tests.
Either way, you review before anything merges.
Code Review: Your Second Pair of Eyes (That Never Gets Tired)
Your team ships 15-20 PRs a day. You review them between feature work. You catch the obvious stuff — naming issues, missing tests, that one function that's 200 lines long. But the subtle things slip through. The N+1 query. The missing index. The API response shape that changed without anyone noticing.
With Burrak AI:
"Review PR #247 in ourcompany/api-service. Check for security issues, performance regressions, breaking API changes and adherence to our coding standards."
Burrak's review covers ground most humans miss:
- Security: SQL injection vectors, exposed secrets in config files, improper authentication checks, missing rate limiting
- Performance: N+1 queries, missing database indexes, unnecessary allocations in hot paths, synchronous calls that should be async
- Breaking changes: API contract violations, changed response shapes, deprecated endpoints still in use, missing migration scripts
- Style: Naming conventions, code organization, documentation completeness, consistency with existing patterns
- Test coverage: Untested edge cases, missing integration tests, flaky test patterns, tests that pass by accident
Every finding includes the exact file, line number, severity level, and a suggested fix. The review comes back to you rather than being posted or committed: the GitHub connector reads code and pull requests, and a person decides what changes. The AI Code Reviewer role sets an agent up for exactly this.
The best part? Burrak doesn't get tired on PR #14. It reviews the fifteenth PR with the same attention as the first.
Documentation: Write It Once, Keep It Updated Forever
Your API has 47 endpoints. The docs cover maybe 30. The ones that are documented reference parameters that were renamed six months ago. New team members spend their first week reading code instead of docs. Everyone agrees documentation is important. Nobody updates it.
With Burrak AI:
"Generate complete API docs for ourcompany/api-service. All endpoints, request/response schemas, auth requirements, error codes and usage examples. Output an OpenAPI 3.0 spec and Markdown."
This is reading work, so a remote agent can do it through the GitHub connector. It reads the codebase — route definitions, middleware, request validators, response serializers, error handlers, authentication decorators — and generates:
- OpenAPI 3.0 specification — importable into Postman, Swagger, or any OpenAPI-compatible tool
- Markdown documentation — endpoint descriptions, request/response examples, authentication details, error code reference
- Inline code comments — for complex business logic that benefits from explanation
And because agents run on a schedule, you can have it re-read what merged each week and tell you which sections of the docs are now out of date — with the corrected text ready to paste. Documentation stops being a separate project and becomes a weekly review. The AI Technical Writer role is set up for exactly this.
Test Suite: From 34% Coverage to "Actually Confident"
That critical payment processing module has 34% test coverage. Everyone knows it needs tests. The ticket has been in the backlog for three sprints. Nobody has time.
With Burrak AI:
"Write tests for the payment processing module. Cover the happy path, edge cases (zero amounts, negative amounts, currency mismatches), error states (timeout, declined, insufficient funds) and concurrent transactions. Run them and fix any that fail for the wrong reason."
Tests have to be run to be worth anything, so this is a job for a local agent with shell access, working in your checkout. It analyzes the module, understands the business logic, and generates:
- Unit tests for every function with edge case coverage
- Integration tests for the payment flow end-to-end
- Mock configurations for external services (Stripe, PayPal)
- Test fixtures and factory functions
- Tests for race conditions and concurrent transaction handling
All tests are written in your existing test framework (pytest, Jest, RSpec — whatever you use) and follow your project's conventions, and the agent runs them before handing back. Review them the way you would a colleague's: a test that passes is not the same as a test that checks the right thing.
Feature Development: From Spec to PR
The product team wants two-factor authentication. They've written a 3-page spec. You're looking at 2-3 days of implementation — database migration, API endpoints, frontend components, email templates, recovery flow.
With Burrak AI:
"Implement TOTP-based 2FA in auth-service. Spec: docs/specs/2fa.md. Include the setup flow, login challenge, recovery codes and admin reset. Follow existing patterns in the codebase."
Hand that to a local agent with shell access. It reads the spec. Reads the existing codebase to understand patterns and conventions. Plans the implementation. Then executes:
- Database migration for 2FA fields
- TOTP secret generation and validation
- Setup flow (QR code, verification)
- Login challenge integration
- Recovery code generation and validation
- Admin reset capability
- Email templates for setup and recovery
- Tests for every component
- Updated documentation
You review it properly — this is auth code — tweak what needs tweaking, and merge. A large feature is where an agent's first draft needs the most review, but most of the typing is done while you were building something else.
Why This Works (When Other AI Coding Tools Don't)
You've probably tried AI coding assistants. Copilot autocompletes lines. ChatGPT writes functions you have to copy-paste. Claude Code helps but needs you at the keyboard.
Burrak AI is different in three ways that matter:
1. It's asynchronous. You don't pair-program with Burrak. You assign work and walk away. It's the difference between a junior dev who needs constant supervision and a senior dev you can trust with a ticket.
2. It works in your repository, not a chat window. Remote agents read code, issues and pull requests through the GitHub connector; a local agent with shell access works in your checkout — branches, commits and PRs with your own git credentials. It works the way your team works.
3. It remembers. Burrak learns your codebase conventions, your naming patterns, your architectural preferences. Every task makes the next one faster and more accurate.
What This Costs vs. What It Saves
| Task | Time Without Burrak | Time With Burrak | Saved |
|---|---|---|---|
| Bug fix (medium complexity) | 2-4 hours | 15 min review | 2-4 hours |
| Code review (per PR) | 20-40 min | 5 min review | 15-35 min |
| API documentation | 10-20 hours | 30 min review | 10-20 hours |
| Test suite (per module) | 4-8 hours | 30 min review | 4-8 hours |
| Feature from spec | 2-3 days | 1 hour review | 2-3 days |
Burrak AI Pro costs $9.99/month. If it saves you one hour a month, it has already paid for itself.
What Burrak AI Doesn't Do for Developers
- No in-IDE extension. Burrak AI doesn't have a VS Code or JetBrains plugin. For real-time tab completion and inline suggestions, you'll want a tool like Copilot or Cursor alongside Burrak AI.
- Async, not real-time. The git-native workflow (clone → branch → fix → PR) is powerful for background work but slower for quick, interactive coding. Use Burrak AI for the work that happens while you're away from your keyboard.
- Code review isn't infallible. Burrak AI catches issues humans miss, but it also misses things humans catch. Always review its output — especially for security-critical code.
- Proprietary platform. Burrak AI is not open-source. Your code never trains models, but the platform itself isn't inspectable.
The Real Question
Not "can AI help me code?" The question is: how much of your week is spent on work that doesn't need your brain?
Bug fixes at midnight. Boilerplate PR reviews. Documentation that's perpetually out of date. Tests you know you should write but never do.
These aren't the parts of the job you'll miss. Hand them off. Get back to building.
Try Burrak AI → — desktop, web and mobile, $1 for your first week. First agent running in under 5 minutes.
Tags: AI code review, automated bug fixing, AI for developers, Burrak AI dev tools, code documentation AI, test generation AI