Backed by South Park Commons
Build your software factory
CLI agents are caveman software engineering. XHawk merges the runtime, planning layer, and execution system into a software factory where cloud agents run in parallel with shared context, shared filesystems, and per feature production tracking.
Few humans. Hundreds of agents.
Coordinate teams of agents through a shared interface for planning, execution, reviews, and handoffs.
Build software in batch mode.
Queue work in bulk and execute tasks asynchronously. Most frontier models are up to 50% cheaper in batch execution.
Every task is audited and remembered.
Every agent action is traced, reviewed, and stored, building a compounding knowledge layer across your engineering team over time.
The velocity gap
More code shipped does not mean faster delivery.
Your team adopted AI coding tools. Developers are writing code faster than ever. But cycle times haven't improved. PRs still pile up. Deployments still take days.
The bottleneck isn't individual speed. It's the manual handoffs between planning, coding, review, testing, and deployment. Factory agents eliminate those handoffs entirely.
The software factory
Coding is faster but what about the entire SDLC
The real breakthrough isn't just speed. The real shift is this, software is no longer written. It is produced through autonomous systems.
Companies like Ramp, Block, Stripe, and Spotify have already moved in this direction. They're building internal systems where agents don't just assist engineers, they autonomously execute end-to-end workflows.
This is the beginning of the Software Factory, the evolving AI-native architecture where autonomy drives every stage of software development.
Redesign your SDLC
The answer: agents that run in the cloud factory
A background agent is an autonomous AI process that executes tasks across your development lifecycle without requiring a human at the keyboard. Unlike interactive coding assistants, background agents are triggered by events, run on schedules, or coordinate in fleets.
| Dimension | Coding Assistant | Factory Agent |
|---|---|---|
| Where it runs | Your local machine | Cloud sandbox / Devbox |
| How triggered | You type a prompt | Event, schedule, or fleet |
| Scope | Depends on the engineer | Entire repos & systems |
| Developer role | Driver | Architect & reviewer |
| Availability | When you're active | 24/7 continuous |
Understand the factory components
Multi-Agent Orchestration
Planner, executor, reviewer. Problems break down into specs, get implemented in parallel, and validated before shipping.
Sandboxed Execution
Each agent runs in isolation with its own repo copy and zero risk to production systems.
Context Layer
Agents pull from specs, past decisions, tickets, and live signals like logs and metrics to act accurately.
Curated Capabilities
Edit code, run tests, create PRs, call APIs. Quality of tools matters more than quantity.
Workflow Integration
Integrate with Github, Slack and your cloud infrastructure to run automations 24/7.
Guardrails & Human Review
Agents generate PRs and run tests. Humans define requirements and approve outputs.
Scale your software factory
Agents handle the routine. Engineers focus on architecture, product decisions, and customer empathy.
The factory metaphor is intentional. Every great manufacturing system separates human creativity from mechanical repetition. Software is next.
| Metric | Value |
|---|---|
| Agent availability | 24/7 |
| PR throughput | 10× |
| Review latency | <2min |
| Parallel capacity | ∞ |
How the factory works
Agents as Teammates
Agents have identities, appear on your board, comment on tasks, and surface blockers. They don't just execute, they participate.
Snapshots
Every PR is fully traceable. Sessions, decisions, and context become indexed knowledge your team builds on.
Autonomous Execution
Set the goal, let the system run. Agents manage the full lifecycle from enqueue to completion with no micromanagement.
Reusable Skills
Every solution becomes a reusable capability. Deployments, reviews, migrations, once solved, become available to your whole team.
Unified Runtimes
One control plane for all execution. Run agents securely in the cloud with real-time monitoring and zero context switching.
Multi-Workspace
Each workspace has its own agents, features, and config, fully isolated yet scalable from small teams to large orgs.
From codebase to software factory in minutes
Step 01
Connect your repo
Connect your GitHub repository and let agents work inside isolated, fully secure sandboxes.
Step 02
Configure agents
Choose from pre-built agents or define custom behaviors for your workflows.
Step 03
Set triggers
Set automations or work items on a Kanban board, then trigger agents on schedules, GitHub events, CI failures, or webhooks.
Step 04
Ship continuously
Agents handle the mechanical work. Your team focuses on what only humans can do.
Step 05
Custom automation
Work with our Forward Deployed Engineers to automate your company specific requirements.
Usage-based
Pay for execution time. Not tokens.
Most AI platforms charge across a moving target: model tiers, token usage, context windows, tool calls, and inference pricing. Costs become unpredictable fast, and finance teams struggle to forecast spend.
XHawk aggregates tokens and sandbox usage into one number. That's your bill. You can also use BYOK if that works better for you.
Every agent run is metered down to the second and aggregated across your team for the billing period. Just pay for productive compute time, whether agents are working in parallel, overnight, or on demand.
| Pricing | Cost |
|---|---|
| Simple pricing | $0.15/min batch mode (Async, parallel) |
| $0.30/min interactive (Real-time) |
Who it's for
Helping tech teams ship faster
For Founders
Launch products and features faster without scaling headcount linearly.
Use teams of agents to turn specs into production-ready workstreams, so engineers can focus on the highest-leverage problems.
For CTOs
Increase engineering velocity while keeping teams lean.
Automate testing, reviews, documentation, and repetitive workflows with coordinated agents that help teams ship faster with confidence.
For Senior Architects
Offload repetitive implementation and operational work to agent teams.
Move smaller features, migrations, and refactors in parallel while focusing your time on architecture, system design, and critical technical decisions.
For VP Engineering
Track token usage, infrastructure cost, and execution efficiency per feature.
Apply the same operational discipline as cloud infrastructure to your agent fleet, with full visibility into spend and efficiency.
Where does your engineering org stand?
Nobody can fully predict the endgame of software engineering. But we do know everything has already changed. The question is whether your organization is building a software factory, or still running a cottage.