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AI coding agents speed up completion by 55% and catch 40% more bugs before merge (GitHub 2024 survey). Get suggestions, reviews, and refactors right in your IDE.
Faster code completion (developers with AI assist)
Fewer bugs in review (AI-assisted first pass)
An AI coding agent is a model that works on a repository through tools — reading files, running tests, opening pull requests — rather than answering in a chat window. In a team it is most useful when it is narrow and verifiable: a first-pass review on every pull request against your written standards, unit tests generated for the code that changed and kept only if they pass and add coverage, a data-migration mapping proposed and confirmed by a person. The autonomous agent that takes a ticket and ships a feature exists, and the teams running it well are the ones that wrapped it in the same gates: comment rights before merge rights, tests before trust, a person on every merge.
If two of these are yours, the processes below are where to start. Free audit — or read on.
What we build
from $247/ month
3 ready coding workflows on n8n — set up, hosted and maintained for you. You keep the JSON.
from $2,000one-time
Built for your systems and rules on Claude and n8n. Live in 2–4 weeks with documentation and a walkthrough; maintenance optional from $99/month.
60%
Faster Legacy Java to Kotlin Conversion — AI Coding Agent for Enterprise Migration
What you get
The 4 outcomes teams name first — measured on the process, not promised in a deck.
Faster implementation with inline suggestions and generation
Fewer bugs and style issues via automated review and refactors
Less context-switching—AI works inside your editor and repo
Better documentation and tests with one-click generation
Real deployments
Real outcomes from real builds — not marketing copy.
All case studiesFaster Legacy Java to Kotlin Conversion
How a fintech company used an AI coding agent to migrate 180K lines of Java to Kotlin, completing in 8 weeks instead of the estimated 20 weeks with manual conversion.
Read deployment →2×Faster Code Reviews
How a 40-person development agency deployed AI coding agents to cut code review time in half while catching more bugs.
Read deployment →How it works
3 steps, none of them yours to code.
Use cases
Problem
Developers spend hours writing repetitive boilerplate and translating specs into code. Context-switching between docs and the editor slows velocity.
Solution
Describe what you need in natural language or paste a spec. The AI agent generates functions, tests, and boilerplate in your IDE with full repo context.
What you get
How to get started
Tools: GitHub Copilot, Cursor, Cody
Problem
Debugging is time-consuming: reading stack traces, reproducing issues, and searching for similar cases. Junior developers spend disproportionate time here.
Solution
Paste an error or highlight failing code. The agent traces the root cause using repo context, suggests fixes, and can apply them inline.
What you get
How to get started
Tools: Cursor, GitHub Copilot, Codeium
Problem
Triaging, finding the root cause, writing the code, and submitting PRs for hundreds of tickets takes away from core product work.
Solution
An autonomous agent reads an issue, navigates your repository, researches the problem, writes the fix, and opens a PR with tests.
What you get
How to get started
Tools: Devin, SWE-agent, Cody
Problem
Developers skip writing tests under deadline pressure, leading to low coverage and fragile releases. Retroactively adding tests is tedious and often deprioritized sprint after sprint.
Solution
The AI agent reads function signatures, docstrings, and usage patterns to generate meaningful test cases—including edge cases, error paths, and integration scenarios. Tests follow the project's existing framework and naming conventions so they integrate seamlessly into CI.
What you get
How to get started
Tools: Cursor, Codium, GitHub Copilot
Problem
Documentation falls out of date the moment code changes. Engineers avoid writing docs because it's tedious, and new team members waste days reverse-engineering undocumented systems.
Solution
The AI agent parses source code, type definitions, and commit history to generate and update docstrings, API references, and high-level architecture docs. It runs as a CI step or IDE plugin, flagging undocumented functions and proposing updates when signatures change.
What you get
How to get started
Tools: Mintlify, Cursor, Cody
Workflows we build
Each blueprint shows the trigger, the steps with the n8n nodes named, the guardrails and an importable template — and what it costs to have us run it for you.
All blueprints| Workflow | Department | Steps | Managed | Custom build |
|---|---|---|---|---|
| Automated Code Review with AIEvery PR gets a substantive first review within minutes of opening: specific comments with the reasoning, a risk label, and a summary of what changed and what to look at. Human reviewers pick up PRs already triaged, review time per PR falls, and the standards document is finally enforced on every change. | Engineering | 8 | $247/month | $3,500–6,000 one-time |
| AI Unit Test GenerationEvery PR that adds untested logic gets a companion PR with passing tests written in the repo's style, usually within the same hour. Engineers review tests instead of writing them from scratch; coverage on changed code rises; and the tests that survived actually run, because failing ones were never proposed. | Engineering | 8 | $247/month | $3,500–6,000 one-time |
| Data Migration AutomationA confirmed mapping with every field's transformation and validation rule, a dry run that reconciles record counts and sums, a batch cutover with progress and error reports, and a rollback plan that was tested. The migration runs on schedule; the team spends its time on the exceptions the validation found. | Engineering | 8 | $297/month | $5,000–12,000 one-time |
Ready to ship?
Tell us your workflow — the free audit sends a one-page plan with scope and timeline in minutes. No call.
Is this for you?
Not quite? Take a look at ai marketing agent — the closest neighbour.
Background
Assistants that integrate into the development workflow to write code, review pull requests, fix bugs, and refactor codebases. Full repo context and IDE integration provide suggestions that align with your patterns and best practices.
AI coding agents bring full repo context and IDE-native completion to your workflow. GitHub's 2024 survey found 55% of developers report faster completion with AI assist, and automated review catches 40% more bugs before merge. Use them for first-pass review, refactors, and documentation—humans own architecture and final approval.
Unlike a generic chatbot or manual process, an AI coding agent runs autonomously and integrates with your existing tools. Gartner projects that by 2026, over 80% of enterprises will have used GenAI APIs or applications.
Build, buy, or done-for-you
Pick the path that fits your team and timeline. Most companies start with one and grow into the others.
Wire up a ready platform yourself. Best for hands-on teams comfortable configuring software.
We scope, build, and deploy your agent — integrated with your CRM and tools. Best for teams that want it live in days, not months.
See the ROI and cost before you commit — useful for justifying the decision internally.
Prefer to build it yourself?
If you’d rather DIY, these are the tools we’d reach for. Each lets developers run an AI coding agent without writing code.
| Tool | Best for |
|---|---|
| IDE integration and real-time suggestions | |
| AI-first code editor with full context | |
| Autonomous AI software engineer | |
| Open-source autonomous coding | |
| Codebase-aware AI assistant | |
| Free AI code completion | |
| Privacy-focused code completion |
We may earn a commission when you sign up via our links. About the studio
Vendor directory
| Vendor | Starting price | Pricing model | Best for | Free tier |
|---|---|---|---|---|
| GitHub Copilot | $10/seat/mo | per seat | Enterprise teams on GitHub | Yes |
| Cursor | Free | per seat | Individual devs and small teams | Yes |
| Claude Code | Custom | usage based | Engineers comfortable with CLI; complex refactors | — |
| Cody (Sourcegraph) | Free | per seat | Enterprises with monorepos / huge codebases | Yes |
| Windsurf (Codeium) | Free | per seat | Devs wanting free-tier agentic coding | Yes |
| Devin (Cognition) | $500/mo | tiered | Teams routing routine eng work to AI | — |
Run the numbers first
Put your own volumes in before you ask for the plan — every calculator is free and needs no sign-up.
FAQ
You're already coding—it assists you. GitHub's 2024 Octoverse reports the majority of developers now use AI-assisted coding. You don't write scripts to use it: install, configure style or scope, then use it via your IDE. It writes or suggests code; you stay in flow.
Coding agents live in your editor with access to your full repo, so they suggest edits in place, follow your patterns, and run reviews. Stack Overflow's 2024 survey found IDE-integrated tools preferred 2:1 over standalone chat for code tasks. Built for developer workflow, not one-off prompts in a separate tab.
It depends on the tool. Some process locally or in a privacy-focused way; others send snippets or context to the cloud. Check each vendor's privacy and data policy. On-prem and air-gapped options exist for sensitive codebases.
It augments it. GitHub's 2024 research found AI-assisted review catches roughly 60% of common issues before human review. Obvious bugs and style get handled; human review remains essential for architecture, security, and domain logic.
Give it comment and branch rights, not merge rights; require its changes to pass the existing suite plus the tests it generated; log every action with the file, the rule or the ticket it cited; and measure two things weekly — the acceptance rate of its changes and the reviewer edits on them. An agent whose acceptance rate is falling is drifting from your standards, and the log shows where.
The diff, its context and the relevant tests go to the model provider for each call, under your data-processing terms; nothing is retained for training under a zero-retention agreement, which the major providers offer. Repositories that cannot leave your network run the same workflows against a model hosted in your own cloud account. Ask any vendor which files leave, to whom, and under what retention — the answer should be specific.
It replaces the first pass — style, missing tests, an obvious null path, a breaking change to a caller — so the human review starts at the design. Teams that let it be the only review ship the bugs that need context: a requirement misread, a migration that is correct and wrong. The workflow here posts comments with the evidence and leaves the approval to a person.
Choose your path
Use AI in your own day-to-day — free tools, copy-paste prompts. No engineering needed.
Best AI tools for developers →You deploy it across a teamThe 3 engineering processes we install in the tools the team already runs — each priced, with a team package — and the free audit that names the first one.
Engineering automation for teams →Ships in days
Tell us your workflow and the free audit sends a one-page plan for developers — scope, recommended agents, and a go-live timeline — by email within minutes. No call, no obligation.