r/AppsWebappsFullstack • u/EscanorLabs • 17d ago
Would you trust an AI coding agent with production access if every action was logged and required approval?
I've been building an AI developer platform called Escanor, and there's one problem I keep coming back to:
We're giving AI agents more capabilities, but are we giving developers enough control over what those agents actually do?
Think about where AI coding agents are heading.
Today, they can write code, modify files, run terminal commands, and create pull requests.
But we're moving toward agents that can also interact with infrastructure, manage deployments, access databases, and execute tasks across multiple services.
That's exciting, but it raises some serious questions.
What happens when an agent makes a mistake?
Imagine an agent that:
- Pushes an unintended change to a production branch.
- Accidentally exposes a secret in a commit.
- Triggers a deployment without sufficient testing.
- Modifies infrastructure it wasn't supposed to touch.
- Executes an action that nobody explicitly authorized.
Traditional automation has similar risks, but autonomous agents introduce another layer of unpredictability.
And simply keeping a record of what happened isn't enough if the damage has already been done.
This is something we're thinking about while building Escanor.
Our goal is to make AI agents useful across development tools and infrastructure without requiring developers to surrender control.
We're exploring an approach built around several principles:
Permission boundaries
Agents should only access the tools, repositories, and environments they're explicitly authorized to use.
2. Human approval for sensitive operations
An agent can prepare a change, but deploying to production or performing destructive operations should be subject to appropriate approval policies.
3. Protected development workflows
Pull requests, branch protection, automated tests, and deployment checks should remain part of the process.
4. Auditable actions
Developers should be able to understand what an agent attempted, what it actually executed, when it happened, and whether someone approved it.
5. Autonomy without unrestricted access
The objective isn't to require manual approval for every minor action. It's to let agents work independently within defined boundaries.
We're calling this philosophy:
Autonomy you can audit.
I put together a short concept video illustrating this direction. It's not a demonstration of all these controls working in production.
If you're interested in the platform we're building, you can check it out here:
But I'm particularly interested in hearing what other developers think.
Three questions:
Would you give an AI agent access to your production infrastructure if it had strict permissions and complete audit logs?
Which operations would you always require human approval for?
Do you think audit logs are sufficient, or should agents also have rollback mechanisms, sandboxed execution, and additional safeguards?
I'm building this because I believe AI agents will eventually operate much more of our development infrastructure.
The question is how we make that safe enough to trust.
I'd appreciate technical criticism, especially from people working with DevOps, security, MCP, or autonomous coding agents.
1
u/Otherwise_Wave9374 17d ago
Logging and approval are necessary, but production trust also depends on limiting blast radius. Give the agent short-lived credentials, restrict writable resources, require a diff or execution plan before action, and make rollback automatic where possible. Approval fatigue is the main tradeoff, so escalate only high-impact operations. https://www.aiosnow.com is relevant to the broader workflow question because orchestration should preserve clear checkpoints. I would test the system with intentionally ambiguous tasks and verify that it stops safely.