Governed autonomous coding agent control plane

For

Platform and security teams running autonomous AI coding agents inside their own infrastructure

Solves

Autonomous coding agents run without isolation, budget limits, approval gates or a unified audit trail, so teams cannot safely let them act.

Delivers

Approved agent actions under policy

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Run autonomous coding agents under isolation, oversight and auditability.

1. Run autonomous coding agents on tasks and workflows.
2. Support multiple AI coding models and runtimes per agent.
3. Self-host execution on the organization's own infrastructure.
4. Isolate each agent in a sandbox to prevent interference or data leakage.
5. Assign each agent a role with its own model, workspace, memory, tools, skills and permissions.
6. Start agent work from chat platforms, GitHub, schedules and webhooks.
7. Let agents call one another to coordinate work.
8. Show what each agent is allowed to see or do.
9. Filter unnecessary messages with a no-op signal.
10. Version long-running workflows for seamless updates.
11. Manage durable workflow state without external database provisioning.
12. Provide end-to-end observability through a console and existing observability stacks.
13. Enforce AI guardrails, safety policies and per-tool-call rules such as allow reads, approve writes.
14. Pause agents at human-in-the-loop checkpoints when a write requires approval, including scheduled runs.
15. Cap spending with hard budget limits.
16. Gate retries on verifier evidence and support rollback.
17. Capture machine-readable run receipts and a unified audit log with agent identity.
18. Export logs and show a post-run timeline against the original plan.
19. Sequence tool calls with a DAG and orchestrate across distributed systems.
20. Create agents from plain-language descriptions with a generated execution plan.
21. Run headless and give teams oversight of cost and activity.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Agent definitions, repository access, tool permissions, budgets and safety policies

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent definitions

4. 3

   Repository access

5. 4

   Tool permissions

6. 5

   Budgets and safety policies

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Approved agent actions under policy

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Agent ownership, role and permission versions, workspace and memory settings, approval states, budget caps, run receipts, audit exports and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: run autonomous coding agents on tasks and workflows; isolate each agent in a sandbox. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will platform and security teams running autonomous AI coding agents inside their own infrastructure use it to solve "autonomous coding agents run without isolation, budget limits, approval gates or a unified audit trail, so teams cannot safely let them act"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Approved agent actions per oversight hour and unapproved or unexplained actions per run.
4. Measure, then decide. Track approved agent actions per oversight hour and unapproved or unexplained actions per run; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around approved agent actions under policy. Retain the explicit scope boundary: One approved model set and one self-hosted runtime; final code changes and production access remain under human control.

What the build depends on. Agent registry and preview, asynchronous execution jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist security QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved model set and one self-hosted runtime; final code changes and production access remain under human control.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined agent package. Offer a monthly production allowance after repeat demand. Quote complex multi-agent or on-premise deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded approved agent actions under policy. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Run autonomous coding agents under isolation, oversight and auditability. Demonstrate a concrete approved agent actions under policy using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Platform and security teams running autonomous AI coding agents inside their own infrastructure professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample approved agent actions under policy from a small authorized input set, with a transparent calculation of approved agent actions per oversight hour and unapproved or unexplained actions per run and no promised savings.

The first 30 days

1. Week 1: interview five platform and security teams running autonomous AI coding agents inside their own infrastructure and inspect a recent example of autonomous coding agents run without isolation, budget limits, approval gates or a unified audit trail, so teams cannot safely let them act.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure approved agent actions per oversight hour and unapproved or unexplained actions per run, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Approved agent actions per oversight hour and unapproved or unexplained actions per run. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Approved agent actions per oversight hour and unapproved or unexplained actions per run; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs approved agent actions under policy. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved agent roles, policy rules and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for platform and security teams running autonomous AI coding agents inside their own infrastructure. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Runtime, AgentConnect, Inferable, MartinLoop and Lunen.ai, plus internal scripts and generic CI runners. Compare this product with the buyer's present method on approved agent actions per oversight hour and unapproved or unexplained actions per run. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model and runtime usage, sandbox compute, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of approved agent actions under policy. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.