Organization context
Make organization-level guidance available to every eligible run.
Give every AI coding run the right repository context.
MergeLoom makes context part of the platform instead of leaving every developer to rebuild instructions from memory.
Works with
Jira GitHub GitLab M monday.dev Linear Azure Boards Azure Repos
Jira GitHub GitLab M monday.dev Linear Azure Boards Azure Repos
Jira GitHub GitLab M monday.dev Linear Azure Boards Azure Repos
Jira GitHub GitLab M monday.dev Linear Azure Boards Azure Repos
Repo rules
Use repository-specific constraints and commands to shape the change.
File context
Keep the agent focused on the folders and files that matter.
Run instructions
Standard instructions reduce inconsistent agent behavior from ticket to ticket.
Audit context
Keep a trail of the guidance that shaped the AI run.
Give AI the right context
Better AI output starts before the model writes code: tickets, comments, docs, repository rules, and file scope are attached to the run.
Less guesswork
The agent starts with the acceptance criteria, codebase rules, and file scope it needs, instead of guessing from a vague ticket.
Reusable rules
Organization and repository guidance can be applied every time, so output is not dependent on which developer remembered which prompt.
Better review trail
Reviewers can understand the context that shaped the change, making AI-generated code easier to inspect and challenge.
Make the important instructions repeatable
Tenant instructions
Set broad rules that should apply across projects and repositories.
Repository guidance
Keep architecture notes and constraints close to the codebase they affect.
Run instructions
Control how the worker approaches setup, implementation, and validation.
Review expectations
Tell the agent what reviewers expect before it opens a PR or MR.
Shape every run
Use AGENTS.md, architecture docs, Markdown folders, context repositories, or Confluence Cloud to steer implementation before code is written.
Docs become guardrails
Architecture notes, coding standards, domain rules, and review expectations can guide the run instead of sitting unused in a wiki.
Fewer weak choices
The agent has more of the same context your engineers rely on, reducing shallow fixes and vibe-coded implementation choices.
Context is inspectable
Teams can see which guidance was available to the run, making the output easier to explain during review.
Runs with
CX Codex Claude Vertex AI AWS Bedrock AZ Azure Foundry API OpenAI-compatible
CX Codex Claude Vertex AI AWS Bedrock AZ Azure Foundry API OpenAI-compatible
CX Codex Claude Vertex AI AWS Bedrock AZ Azure Foundry API OpenAI-compatible
CX Codex Claude Vertex AI AWS Bedrock AZ Azure Foundry API OpenAI-compatible
See what else MergeLoom can do.
Connect more of your stack, improve context, validate output, and keep audit evidence across every AI coding run.
Try one controlled AI coding workflow.
Start with one tracker, one repository, and one validation path before rolling AI coding across the team.