Bug fixes and debugging
Agents read the issue, code, tests, CI, and logs. When they find the root cause, they propose or deliver a PR with evidence.
Autonomous Engineering Delivery
Faktorial operates the delivery loop inside your repo: investigate the work, build the change, prove it with tests and CI, prepare the PR, and capture what it learned. Run it with your team, or have Asynkron operate it for you.
computer Runs locally
GitHub-native
verified_user Approval gates
psychology Escalates when uncertain
history Full audit trail
GitHub-native agent factory
Automate your backlog.
Faktorial takes issues from “should get done” to tested PR, merge, and lessons learned back into the system. You choose labels, branch, quality gates, and how autonomous it is allowed to be.
3000+
internal issues delivered
17 min
internal median issue resolution
PR-first
human approval by default
A backlog is already a work order. Faktorial makes it executable.
The simple pitch is not “AI writes code”. It is that the team’s existing work can flow through a controlled factory: debugging, development, verification, and learning.
Development
UI, backend, integrations, migrations, and test coverage move from acceptance criteria to branch, commit, and PR.
Self-learning
Retries, friction, and good decisions become ADRs, new agent rules, or tasks that improve the next delivery.
You decide the scope
Run on selected labels, selected sizes, the full backlog, or only a pilot. PR-only or auto-merge is a control.
Control plane
Faktorial sits between work and workers.
GitHub remains the source of truth. Faktorial reads the work, applies rules and compliance, then dispatches the right agent runtime with the right process and evidence requirements.
GitHub Issues, comments, PRs, and workflows flow into the same control plane.
Rules, compliance specs, MCP integrations, monitoring, and process live in Faktorial.
Claude Code, Codex, and CoPilot execute under the same operating envelope.
Layer 01 / GitHub
source
Issues + Comments
delivery surface
Pull Requests
runtime signal
Workflows
Layer 02 / Center
Faktorial
control
Orchestration
visibility
Monitoring
extension
3rd-party MCP integration
policy
Rules
governance
Compliance specs
delivery
Process
Layer 03 / Agents
runtime
Claude Code
runtime
Codex
runtime
CoPilot
From issue to lesson
Every job gets a traceable life.
Faktorial isolates each issue in its own branch and worktree. Investigation sets the boundaries, build does the work, verify keeps scope and test evidence honest, deploy handles merge, and learn writes back what the system should remember.
One issue, one concern, one PR.
Tests and acceptance criteria must line up.
New friction becomes new knowledge, not scope creep.
Autonomy is a control. Not a religion.
You choose what may happen, where it may happen, and when a human must approve the work.
Scope builder
pilot-safe
Branch target
develop/faktorial-pilot
Allowed action
Open PR, wait for approval
Self-learning
The most important part is what happens after merge.
Faktorial captures signals from development and turns them into usable assets: rules for agents, ADRs for humans, and new issues for improvements that do not belong in the same PR.
01CI was slow or flaky.
02Learn analyzes the pattern and writes the root cause.
03The system creates a task: optimize test performance.
04The next agent run gets a new preventive rule.
Plug in where the team already works.
GitHub, CI, local logs, and the Faktorial dashboard are the core. Datadog and Grafana connect as integrations for teams already living in logs and metrics.
Issues, labels, branches, PRs, comments, and CI state.
Local state, workers, worktrees, issue logs, and evidence.
Logs, metrics, and traces from runtime-owned boundaries.
Natural ingest surface for production signals.
Dashboard layer for teams already living in metrics.
Fit_Guide
Built For Backlog-Heavy Teams.
With Clear Review Paths.
Faktorial works best when your team already has a GitHub workflow, a queue of engineering work, and enough context in issues or code for implementation to be reviewed against clear expectations.
Good Fit
✓Structured GitHub issues with acceptance criteria
✓Teams that need more throughput without adding headcount
✓Regulated or audit-heavy repositories
✓Recurring bugs, refactors, migrations, and test work
Keep Human First
×Ambiguous product discovery with no clear outcome yet
×Architecture calls without an accountable owner
×High-risk production changes without a review path
Best for high-volume engineering work.
bug_report
Backlog & bugs
autorenew
Refactors
swap_horiz
Migrations
science
Test coverage
gavel
Compliance gaps
update
Legacy modernization
Pilot_Proof
Start With 10 Real Issues.
Measure What Ships.
The first proof should come from your repo, not a staged demo. A pilot uses a bounded set of issues and reports the evidence engineering leaders actually care about: accepted PRs, CI results, review rework, cycle time, and escalation quality.
Acceptance
How many PRs were accepted, merged, or returned for rework.
Cycle Time
How long issues took from pickup to review-ready pull request.
Evidence
Which tests, CI checks, and trace links backed each delivery.
Escalations
Where the system paused for human judgment instead of guessing.
Service_Models
Two Ways To Ship.
One Standard Of Proof.
Use Faktorial inside your existing GitHub workflow with your engineers reviewing PRs, or use Faktorial Managed when you want Asynkron to run the delivery loop. Same pipeline, same quality gates, same traceability.
handshake
Your Team + Faktorial
Best when you already have a development team and want more delivery capacity inside the workflow you use today. Your team owns priorities, architecture, reviews, and merge decisions. Faktorial handles scoped implementation work.
✓Your team owns priorities and merge decisions
✓Faktorial handles scoped implementation issues
✓PRs arrive with tests and evidence
✓Works inside your existing repo workflow
engineering
Faktorial Managed
Best when you want Asynkron to run the operating loop. You file or approve issues; senior engineers supervise Faktorial, handle escalations, and deliver review-ready code.
✓Asynkron runs the pipeline
✓We supervise agents and handle escalations
✓You approve scope and merge decisions
✓Capacity scales by backlog, not headcount
Elevator pitch
The backlog becomes a production system.
The point is simple: Faktorial is not a chatbot and not a code generator. It is a controlled delivery factory that can work continuously on the backlog you already have.
Asynkron_Origin
Created By The Team Behind Proto.Actor.
Asynkron brings distributed-systems experience from high-scale, mission-critical platforms across finance, industrial automation, and real-time communications into Faktorial's delivery model.
Flagship Framework
Proto.Actor is Asynkron's actor framework for .NET and Go, built for resilient distributed systems with message-driven actors, virtual actors, and clustering.