Operations that run themselves
Create coordinated swarms from a single description, or deploy vetted swarm blueprints from the marketplace. Agents working in parallel and sequence with quality gates, approval checkpoints, and automatic retry. From SEO audits to code reviews. From email campaigns to infrastructure monitoring. Your swarms, running on your schedule.
Set it and forget it
Need an SEO audit every morning? A coding review pipeline on every PR? Email campaigns that adapt to engagement metrics? Infrastructure monitoring that pages you at 3 AM? Describe the schedule in language. Agents handle the execution.
Every scheduled operation has approval gates. Critical actions wait for your confirmation. Routine tasks run autonomously. You define the boundary in one sentence.
SEO Audit Swarm
Code Review Pipeline
Churn Risk Monitor
Reaches you anywhere
A critical alert goes to Slack immediately. A weekly summary lands in your inbox Monday morning. An anomaly triggers a webhook to your internal system. You define the rules in language. Agents handle the routing.
Slack
Real-time alerts
Summaries & reports
Webhooks
System integration
In-App
Dashboard notifications
Playbooks, not guesswork
Not every task should be open-ended. Some workflows are better as guided conversations -- step by step, question by question, with the agent collecting what it needs and building as it goes. Dashboard setup. Data source connection. Chart creation. Onboarding a new team member.
Playbooks are conversation scripts that agents follow when structure matters more than freedom. You answer questions. The platform builds the thing. Skip a step if you want. Come back if you change your mind.
Dashboard Setup
Name, purpose, data source, layout, confirmData Source Connection
Type, credentials, test, name, doneChart Builder
Chart type, data, customise, previewAutonomy with boundaries
When one agent delegates to another, the handoff happens inside a sandbox. Timeouts prevent runaway tasks. Depth limits stop infinite delegation chains. Concurrency caps protect your resources. Outputs are truncated if they exceed safe limits. The agent has freedom to act -- within the boundaries you set in one sentence.
Timeout
30s max per delegation
Depth Limit
2 levels of sub-delegation
Concurrency
3 parallel tasks max
Output Cap
50K chars per response
Agents that write tested code
The coding swarm follows a seven-phase TDD pipeline. Plan the architecture. Write failing tests first. Write the minimum code to pass. Refactor for quality. Self-correct any regressions. Validate with a full test suite. Generate a pull request with full context. Every phase has its own state machine, cost routing, and quality gates. The swarm doesn't just write code — it writes code the way a disciplined engineering team would.
Plan → Red → Green
Architecture analysis, failing tests, then minimum passing implementation
Refactor → Self-Correct
Clean up code quality, detect and fix regressions automatically
Validate → PR
Full test suite verification, then a pull request with complete context
A scratchpad that runs
Sometimes an agent needs to compute, not just describe. DeepHarness gives agents a sandboxed Python environment where they can fetch data, transform it, run calculations, and return results -- all within a single conversation. Variables persist across cells. Packages install automatically. Credentials inject from your connected data sources without ever being exposed to the model. The coding swarm uses this same sandboxed environment — every TDD phase executes in isolation with full credential injection and progress heartbeats.
Ask an agent to pull your Stripe MRR, calculate month-over-month growth, and chart the trend. It writes the code, executes it, and shows you the output. No Jupyter notebooks to manage. No infrastructure to configure. Just results.
Sandboxed Execution
Python code runs in isolated subprocess with timeout and memory limits
Credential Injection
API keys from connected sources injected as environment variables -- never visible to the model
Progress Heartbeat
Long-running scripts emit progress signals that keep the session alive
Session Persistence
Variables and history persist across cells within a named session
Businesses that build themselves
Tell DeepHarness to build you a side business. It will research the market, identify opportunities, set up data pipelines, create monitoring dashboards, configure email outreach, schedule social media, and run the entire operation on autopilot -- with approval gates at every step so you stay in control.
Every operation is fully observable. PII is masked before it reaches any model. Cognitive lineage traces the provenance of every decision. Stuck agents are detected by a doom-loop detector and recovered automatically. Cost tracking shows you exactly what each agent run costs, in real time.
This is not a metaphor. It is a feature.
Set it once. Let it run.
Audits at dawn. Code reviews on every PR. Approval gates at the boundaries you choose.