AI app for it and development · no coding needed
Governed autonomous coding agent control plane
Run autonomous coding agents under isolation, oversight and auditability.
Made for: Platform and security teams running autonomous AI coding agents inside their own infrastructure
What it does for you
The problem
Autonomous coding agents run without isolation, budget limits, approval gates or a unified audit trail, so teams cannot safely let them act.
What it gives you
Approved agent actions under policy
What you give it
Agent definitionsrepository accesstool permissionsbudgetssafety policies
Build your own version of Runtime, AgentConnect and more
One app with what these 5 AI tools do, yours to keep and change: Runtime, AgentConnect, Inferable, MartinLoop, Lunen.ai.
Everything these tools do, in one app
- Agent execution Runs autonomous AI coding agents to perform tasks and workflows.Found in Runtime, AgentConnect, Inferable and 2 more
- Multi-model support Allows using different AI coding models or runtimes for agents.Found in Runtime, AgentConnect
- Self-hosting Lets organizations run agents on their own infrastructure.Found in Runtime, AgentConnect, Inferable
- Sandboxed execution Isolates each agent's activity to prevent interference or data leakage.Found in Runtime
- Role-based configuration Assigns each agent a role and configures its model, workspace, memory, tools, skills, and permissions independently.Found in AgentConnect
- Trigger from chat Starts agent work by tagging or messaging in chat platforms like Slack, Discord, or Telegram.Found in AgentConnect
- Trigger from GitHub Starts agent work from GitHub pull requests, issues, or conversations.Found in AgentConnect
- Trigger from schedules Starts agent work on a schedule.Found in AgentConnect
- Trigger from webhooks Starts agent work via webhooks.Found in AgentConnect
- Agent-to-agent calls Allows agents to call one another to coordinate work.Found in AgentConnect
- Permission visibility Shows what each agent is allowed to see or do.Found in AgentConnect, Lunen.ai
- No-op signal Filters out unnecessary messages by having agents return a no-op signal.Found in AgentConnect
- Workflow versioning Manages multiple versions of long-running workflows for seamless updates.Found in Inferable
- Managed state Handles state management for durable workflows without external database provisioning.Found in Inferable
- End-to-end observability Provides comprehensive monitoring and debugging through a developer console and integration with existing observability stacks.Found in Inferable
- On-premise execution Runs workflows on your own infrastructure with outbound-only connections.Found in Inferable
- AI guardrails Ensures safe and compliant automation.Found in Inferable
- Composability Enables flexible workflow design.Found in Inferable
- Distributed orchestration Manages resources efficiently across distributed systems.Found in Inferable
- Budget caps Sets hard limits on spending to prevent runaway costs.Found in Runtime, MartinLoop
- Verifier-gated retries Requires evidence before another attempt proceeds.Found in MartinLoop
- Rollback support Allows rolling back changes or attempts.Found in MartinLoop
- Run receipts Captures machine-readable records of attempts and outcomes.Found in MartinLoop
- Dashboards Provides visibility into cost and activity.Found in MartinLoop
- Headless execution Runs agents without a graphical interface.Found in MartinLoop
- Team oversight Offers team-level visibility and control over agent activity.Found in MartinLoop
- Safety policies Enforces policies for scope and secrets to reduce risky operations.Found in MartinLoop
- Plain-language agent creation Creates agents from plain-language descriptions, generating a detailed execution plan.Found in Lunen.ai
- Policy engine Enforces rules like 'allow reads, approve writes' at the level of individual tool calls.Found in Lunen.ai
- Human-in-the-loop checkpoints Pauses an agent when a write requires approval, including for scheduled runs.Found in Lunen.ai
- Unified audit log Records every action with the agent's own identity, distinguishing user-initiated and agent-initiated steps.Found in Runtime, Lunen.ai
- Exportable logs Allows audit records to be exported for external review.Found in Lunen.ai
- Post-run timeline Shows per-session details of what the agent did against the original plan.Found in Lunen.ai
- DAG sequencing Manages tool-call sequencing using a directed acyclic graph.Found in Lunen.ai
How it works, step by step
- Run autonomous coding agents on tasks and workflows
- Support multiple AI coding models and runtimes per agent
- Self-host execution on the organization's own infrastructure
- Isolate each agent in a sandbox to prevent interference or data leakage
- Assign each agent a role with its own model, workspace, memory, tools, skills and permissions
- Start agent work from chat platforms, GitHub, schedules and webhooks
- Let agents call one another to coordinate work
- Show what each agent is allowed to see or do
- Filter unnecessary messages with a no-op signal
- Version long-running workflows for seamless updates
- Manage durable workflow state without external database provisioning
- Provide end-to-end observability through a console and existing observability stacks
- Enforce AI guardrails, safety policies and per-tool-call rules such as allow reads, approve writes
- Pause agents at human-in-the-loop checkpoints when a write requires approval, including scheduled runs
- Cap spending with hard budget limits
- Gate retries on verifier evidence and support rollback
- Capture machine-readable run receipts and a unified audit log with agent identity
- Export logs and show a post-run timeline against the original plan
- Sequence tool calls with a DAG and orchestrate across distributed systems
- Create agents from plain-language descriptions with a generated execution plan
- Run headless and give teams oversight of cost and activity
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Governed autonomous coding agent control plane with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Governed autonomous coding agent control plane with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data196 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Run autonomous coding agents under isolation, oversight and auditability. For platform and security teams running autonomous AI coding agents inside their own infrastructure, convert agent definitions, repository access, tool permissions, budgets and safety policies into a source-linked console that executes, gates and records every agent action. The benefit is a testable hypothesis, measured through approved agent actions per oversight hour and unapproved or unexplained actions per run; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect agent definitions, repository access, tool permissions, budgets and safety policies, then follow this sequence: 1. Run autonomous coding agents on tasks and workflows. 2. Isolate each agent in a sandbox. 3. Enforce policy at the level of individual tool calls. 4. Pause at human-in-the-loop checkpoints when a write requires approval. 5. Record every action in a unified audit log with agent identity. Resolve uncertain cases with qualified reviewers, approve approved agent actions under policy, and measure approved agent actions per oversight hour and unapproved or unexplained actions per run against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved model set and one self-hosted runtime; final code changes and production access remain under human control. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve agent identity, source attribution, permission accuracy and usage permissions. Named owners approve substantive changes and production scope. One approved model set and one self-hosted runtime; final code changes and production access remain under human control. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One approved model set and one self-hosted runtime; final code changes and production access remain under human control. Implement one approved input format, a bounded representative case set and the first two task modules: run autonomous coding agents on tasks and workflows; isolate each agent in a sandbox. Support the third module with operator review: enforce policy at the level of individual tool calls. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Organization-owned repositories, chat platforms, GitHub, schedulers, webhooks and existing observability stacks. Cloud or on-premise compute, secret stores and CI/CD destinations. Start with file exchange and validate destination specifications before promising direct deployment. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Agent registry and role configuration, Run console with approval checkpoints, Audit and cost dashboard. Use a list of agents with role, model, workspace, memory, tools, skills and permissions, a central run view showing plan, tool calls, approvals and rollback, and a right-hand panel for policy, budget and audit records. Let users compare a run against its original plan and prior versions. Display running, awaiting approval, completed, rolled back and failed states. Provide an exportable audit view with agent identity and user-initiated versus agent-initiated steps. Make the task-specific outcome approved agent actions under policy visible beside its evidence, review state and value baseline.
