Automate the sprint,
not just the code.
Clawdence handles the sprint work your team currently coordinates by hand: epic breakdown, story sizing, outside-in TDD, and internal review. You set the requirement at the start. You approve one clean PR at the end. Everything in between is automated, governed, and traceable.
Less babysitting. Less review churn. Human control at release.
Designed for real teams working in existing codebases.
Founder Note
I built this because I got tired of micromanaging AI coding tools.
The babysitting is not just the code generation itself. It is everything around it - writing the tickets, sizing the work, refining the scope, and then still having to watch the agent line by line to stop it going off-piste. The coordination overhead never went away. It just moved to a chat window.
Modern AI tools are amazing at writing code, but they are terrible at staying on a straight path. Give them a simple feature request, and they often produce architectural bloat, fragmented output, and noisy PRs that shift the burden downstream onto reviewers. Babysitting a chat window to prevent that defeats the entire purpose of automation.
So I built a system with strict boundaries: multi-model consensus before a line of code is written, outside-in TDD enforced at every story, hard cost guardrails, and a single PR handed back for human approval. Not more AI activity. A governed loop that can run without someone watching it.
How The Loop Works
A governed delivery loop between request and release.
The bottleneck is not raw code generation. It is the coordination work around it: sizing, context recovery, test discipline, and keeping review load sane instead of creating a tsunami of low-trust PRs.
Clawdence
It handles the delivery work humans usually have to coordinate by hand
The point is not more AI activity. The point is fewer handoffs, tighter scope control, and cleaner review.
01
Break the requirement into stories, acceptance criteria, and a codebase-aware execution plan.
02
Review the plan with at least two LLMs before implementation proceeds.
03
Run Outside-In TDD on thin story slices and complete the smaller scope reviews internally.
04
Merge the approved story work and assemble one tested, traceable PR for final human review.
Automated inside the feature stream
Story breakdown, planning, consensus review, TDD, internal merges, and packaging the final PR into a sane review state.
Real Example - Clawdence running against Twenty CRM
It reads the codebase before it plans the work.
Generic ticket generation produces generic tickets. Clawdence inspects the existing architecture first, surfaces what it finds, then sequences stories to confirm the riskiest integrations before expanding the feature.
Live Example
PROJ-7
Add duplicate-company warnings to Twenty CRM before a new company is created.
What it found in the codebase
Discovery 01
Twenty already had duplicate-detection infrastructure, so the plan did not invent a new backend path unnecessarily.
Discovery 02
The real missing seam was duplicate lookup against unsaved company input - not duplicate detection itself.
Stories - ordered by risk
Risky integration boundaries first. Optional complexity last.
01
PROJ-8
Confirms integration
Establish the warning step
02
PROJ-9
Finds the real seam
Validate unsaved-input duplicate lookup
03
PROJ-10
Keeps scope thin
Prove service behavior on create payloads
04
PROJ-14
Defers optional complexity
Add fuzzy similarity only after the baseline works
4 of 8 stories shown
Why Teams Trust It
Built for teams that do not want AI creating review chaos.
Bad AI workflows do not just create risky code. They create too much code, too many PRs, and too much review noise. Clawdence is designed to constrain the work before it reaches your reviewers.
Multi-LLM Consensus
At least two models review the plan independently before implementation proceeds. Disagreements are surfaced, not silently averaged.
Escalation Over Guessing
When the system cannot proceed cleanly, it halts and posts to Slack with context. Bad work never reaches your reviewers.
Outside-In TDD
Implementation starts from acceptance criteria at the outermost testable layer. When a full e2e test requires an environment that can't be replicated locally, Clawdence writes it at the right layer and flags it for CI execution, then works inward to prove the behaviour it can verify immediately.
No Production Secrets
Clawdence has no access to production credentials, databases, or live infrastructure. It operates with a scoped GitHub token and never pushes to main.
Cost Guardrails
Hard timeouts on every execution step and LLM call. Epic-level token budgets. If a task exceeds its boundary, it stops and reports - it does not silently spend.
Single-Tenant + BYOK
Your instance runs in your cloud account using your own API keys. No shared infrastructure, no commingled data, no third-party model training on your code.
How It Runs
The operating model behind the philosophy.
Clawdence runs inside your cloud boundary against real repositories. It uses Git worktrees for isolation, Mise for toolchain sandboxing, and hard guardrails on cost and execution time. When it gets stuck, it escalates to Slack instead of guessing.
Isolated Container Deployment
Runs as a container inside your cloud account or in a dedicated single-tenant instance. Your code and credentials stay within the execution boundary - never commingled with other customers.
Git Worktrees & Mise Sandboxing
Each story runs in its own Git worktree so parallel work never collides. Mise provisions the exact toolchain versions your repo needs - Node, Java, Python - without polluting the host. Language-agnostic by design.
Pragmatic Test Fallbacks
When a full e2e test requires an environment that can't be replicated locally, Clawdence writes it at the right layer and flags it for CI execution, then works inward to prove the behaviour it can verify immediately.
Cost & Execution Guardrails
Hard timeouts cap every execution step and every LLM call. Token budgets are enforced at the epic level. If a task exceeds its boundary, it stops and reports to Slack - it does not silently burn through your API spend.
Organizational Memory
When Clawdence encounters a flaky test, an environment quirk, or a repo-specific convention, it records the lesson. Next time it hits the same codebase, it already knows the workaround.
Scoped Slack Intake
Requirements enter through a dedicated Slack channel. Clawdence determines the target repository from context in the message. Initiation and escalation stay visible to everyone in the channel.
See It Running
No black box magic here.
Traceability shows how every story maps back to the original requirement - coverage, acceptance evidence, and delivery status in one place. Clawdence HQ shows the team in motion - agent activity, active code runner logs, sprint progress, running costs and more while the work is underway.