What if AI agents could turn solved edge cases into shared automation?

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1

What if AI agents could turn solved edge cases into shared automation?

I’ve been thinking about a slightly different direction for AI infrastructure agents.

Today, we often imagine AI agents continuously operating infrastructure: checking telemetry, investigating incidents, changing configurations, fixing problems, and so on.

But long term, I don’t think we should want an AI model to solve the same problem thousands of times.

AI inference is relatively expensive.

Automation is cheap.

So maybe the better loop is:

Unknown problem → AI investigates → AI finds a solution → solution is verified → automation is created → future occurrences no longer need AI

Then the AI goes back to monitoring and dealing mostly with new edge cases.

Over time, more and more operational knowledge becomes deterministic automation.

This also made me think about something like a Stack Overflow for AI agents, but machine-oriented rather than just a Q&A website.

When an agent solves a previously unknown infrastructure problem, it could publish a sanitized structured record containing things like:

- environment and software versions

- symptoms and telemetry

- root cause

- remediation

- regression tests

- generated automation

- rollback procedure

- known failure modes

- evidence that the solution actually worked

Other agents encountering the same problem could search this shared knowledge instead of rediscovering the solution from zero.

Solutions could move through states such as:

Proposed → Reproduced → Verified → Automated → Battle-tested → Deprecated

Eventually, frequently encountered edge cases could graduate from “AI reasoning problems” into normal automation.

The interesting part is that this creates a feedback loop where AI gradually reduces the amount of AI inference required to operate a system.

We have been thinking about a similar concept while exploring an idea called Sentinel AI:

AI handles novelty. Automation handles what we already understand.

For infrastructure specifically, I think the architecture could eventually look something like:

Telemetry → AI reasoning → verified remediation → policy gate → automation → observation → shared knowledge

There are obviously hard problems here: security, trust, environment differences, poisoned solutions, secret/data sanitization, verification, version compatibility, and preventing one agent’s incorrect fix from becoming everyone’s incorrect fix.

But if those problems can be solved, a shared registry of verified machine-readable operational knowledge could become very interesting.

Instead of every AI agent learning the same production lessons independently, agents could build on operational knowledge discovered by other agents.

Curious what others think:

Should autonomous agents primarily execute tasks forever, or should one of their main goals be to continuously convert solved problems into deterministic automation?

2

In a way, that’s what Codex does.

Its core use case leverages AI to create more deterministic processes for production.

I wonder if you can leverage it now to help you?

3

Yes, I think Codex is actually a good example of part of the idea.

It can already take a problem, reason about it, and turn the solution into code, tests, scripts, or infrastructure automation — which is exactly the direction I mean.

The part I’m imagining one step further is a runtime control loop:

known problem → existing automation handles it

unknown edge case → AI is activated → investigates and fixes it → verifies the fix → turns that new knowledge into automation

Then the next time the same issue happens, the AI ideally does not need to reason about it again.

So Codex could potentially be one of the “builders” inside such a system today. The interesting question for me is how far we can automate the whole loop safely: detection, context gathering, generation, verification, policy gating, rollout, rollback, and eventually sharing verified remediation patterns between systems.

That’s the part I’d love to experiment with.

4

Sounds like a great direction.

Are you already embedded into an organisation where maybe you can try evolving this idea in real use cases?

Doing this all theoretically is one thing, but applying it practically is what you really need to make concrete steps forward, imho.

5

Yeah you are right for the moment no im working in a small company they dont even think about this things I hope I can one day actually work on this kind of things

6

Or move!

Sounds like advanced DevOps or smart self-healing production automation

7

Yeah practically that is it ,

I use SOL 5.6 and its an amazing tool I think it already have so much untapped capabilities and I think this can be an awesome product it would reduce firefighting in the devops field so much .

8

why not ask your boss if you can experiment, starting with some less critical area?

9

Im kind of new in this company now and yeah the company is struggling a little bit so yeah its hard to convince them to spent money hahahaha

10

On which case look for an area where it might save them money :wink:

11

I know don’t have best slove . your request info is so simple,because the controller need solve more tasks. u can create AI skills for your request , then need design and create AI agent if need cover more diffrent env . if your want to create AI product then u need AI superconnecter for it isn’t little tools.

12

I think I explained the idea a bit too simply before.

What I’m imagining is not really one AI controller trying to solve everything, and also not an AI agent continuously doing every operational task.

The idea is more like this:

Normal operation → deterministic automation handles the system

If something happens that the existing automation does not understand, then AI is activated only for that edge case.

The AI would then:

- collect the relevant telemetry and context

- investigate the unknown problem

- find or generate a possible remediation

- test and verify that remediation

- apply it through controlled policy and safety gates

- turn the successful solution into deterministic automation

After that, if the same problem happens again, ideally the AI should not need to solve it again. The automation should handle it directly.

So over time the system should become more automated and require less AI inference, not more.

Something like:

Known problem → automation

Unknown problem → AI reasoning → verified fix → new automation

And if many systems could safely share verified edge cases and remediation patterns, then one system could potentially benefit from something another system already learned.

So yes, connectors, tools, skills and agents would all be useful parts of it, but the main idea I’m interested in is a self-improving automation loop where AI mostly handles novelty and continuously converts that novelty into cheaper, deterministic automation.

13

I see your point. It sounds like you’re building toward an AI-assisted automation/control system.

The hard part or unknow part is that without knowing the actual control system, environment, telemetry, and data structure, it’s difficult to say what the right agent or skill design should be.

any way , At a high level, I agree: AI can help optimize automation, but the architecture depends heavily on what is being controlled and what data the agent can safely observe and act on.

14

Yes, exactly — and I agree with that limitation.

I don’t think there can be one universal agent architecture that safely controls every environment.

The control layer would need to understand the specific system it is operating on: its telemetry, APIs, state model, permissions, failure modes, and rollback mechanisms.

So I imagine the core concept being generic, while the execution layer is environment-specific.

For example, the same high-level loop could apply to Kubernetes, Azure, Windows infrastructure, databases, or networking:

observe → detect unknown condition → reason → propose remediation → verify → policy gate → execute → observe result → convert successful remediation into reusable automation

But the tools, permissions, telemetry, tests, and safety constraints for each environment would be completely different.

The part I’m most interested in is defining that generic learning/automation loop and then having environment-specific adapters and policies around it.

And importantly, I would want the system to fail closed: if the AI does not have enough trustworthy context or cannot verify a remediation safely, it should escalate rather than act.

So yes, I think we’re basically saying the same thing — the idea is general, but a safe implementation has to be grounded very deeply in the actual system being controlled.

15

Yes—the stronger and more practical direction is not “AI agents should stop doing work,” but that agents should continuously convert repeatable, proven reasoning into safe, reusable automation. A production system should first search a registry of known solutions, validate that the current environment matches the solution’s preconditions, and execute automation when confidence and policy allow; only when no suitable solution exists—or when existing automation fails—should the AI investigate the incident. A newly discovered fix should never become shared automation merely because one agent claims it worked: it should carry structured evidence such as symptoms, telemetry, root cause, affected versions, prerequisites, remediation, verification tests, rollback steps, security constraints, provenance, and observed success rate, then progress through Proposed → Reproduced → Verified → Policy-approved → Automated → Battle-tested → Deprecated. This makes the shared registry more than a “Stack Overflow for agents”; it becomes a machine-readable operational knowledge layer where verified solutions are continuously reused, measured, versioned, and retired when their assumptions become invalid. The key architectural principle is therefore AI handles uncertainty, deterministic automation handles proven knowledge, and a verification/policy layer controls the transition between them. Done correctly, this creates a compounding feedback loop: every genuinely reusable solution reduces future inference, latency, and operational cost, while failures and new edge cases feed back into AI investigation. The goal should not be to eliminate AI reasoning—because infrastructure continually changes—but to ensure that the same problem does not require the same expensive reasoning twice unless the system has evidence that the previous knowledge no longer applies.

16

Yes — this is very close to what I had in mind, and you expressed the production side of it better than I did initially.

The registry-first approach is especially important. The AI should not immediately reason from scratch if a proven remediation already exists. It should first check whether the current environment satisfies the solution’s preconditions, whether the evidence is still valid for the versions and state involved, and whether policy permits execution.

I also strongly agree that one successful run should never be enough to promote something into shared automation. The evidence, provenance, rollback path, security constraints, observed success rate, and lifecycle of the solution all need to be part of the knowledge itself.

That is probably the key distinction: the registry is not simply a collection of answers, but a continuously validated operational knowledge layer.

And yes, the principle you stated captures it very well:

AI handles uncertainty. Deterministic automation handles proven knowledge. Verification and policy control the transition between them.

That is exactly the direction I find interesting — not removing AI from operations, but making the system progressively smarter about when AI is actually necessary.

17

I agree. I think this becomes even more interesting when we stop treating the registry as a static repository and instead model it as a governed, evidence-backed operational memory system.

The critical distinction is between knowing a solution, trusting a solution, and being authorized to execute a solution. Those are three different states.

A registry entry should therefore represent more than:

Problem → Solution

It should represent something closer to:

Problem signature → Preconditions → Solution → Evidence → Provenance → Risk → Policy → Execution history → Rollback → Current validity

This allows the system to reason about the applicability of knowledge rather than merely retrieving it.

For example, a remediation may have a 99% historical success rate, but that statistic alone should not make it executable. The system still needs to ask:

Is the current environment equivalent to the environments where the evidence was generated?

Is the software version compatible?

Have the underlying assumptions changed?

Has the remediation been modified since validation?

Is the blast radius acceptable?

Is rollback actually available and tested?

Does current policy authorize this action?

Has recent telemetry shown degradation?

This suggests that the registry should behave more like a control plane for operational knowledge than a conventional knowledge base.

I would also make the promotion path explicit:

Unknown → AI-generated hypothesis → Candidate solution → Validated solution → Trusted solution → Policy-approved automation → Continuously monitored automation

And the reverse path must be equally important:

Drift detected → Confidence reduced → Automation suspended → Revalidation → Reapproved or deprecated

That creates an important property: trust is not permanent.

A solution earns trust through evidence, but it must continue earning that trust as the environment changes.

I also think the architecture should distinguish between three different kinds of uncertainty:

1. Epistemic uncertainty — “We don’t know what the problem is or how to solve it.”

→ AI reasoning is appropriate.

2. Applicability uncertainty — “We know this solution, but we’re not sure whether it applies to this environment.”

→ Verification and environment analysis are required.

3. Authorization uncertainty — “The solution may work, but are we permitted to execute it?”

→ Policy and governance must decide.

That separation prevents the AI from becoming the authority over its own actions.

In other words, the AI can propose and explain, but evidence establishes trust, policy establishes authority, and deterministic execution establishes repeatability.

The most powerful consequence is that the system doesn’t merely accumulate answers. It accumulates validated operational capability.

Over time, the architecture should therefore move from:

AI solving problems

toward:

AI discovering new capabilities → the system validating those capabilities → the organization encoding them as governed automation → telemetry continuously determining whether they remain trustworthy.

At that point, the system is not simply “using AI for operations.” It is building an institutional memory of what works, under which conditions, with what evidence, under whose authority, and with what recovery mechanism.

And that is where I think the real opportunity lies: not autonomous AI that acts on everything, but an architecture where autonomy expands only as evidence, verification, and governance justify it.

18

I think this is exactly the right way to formalize it.

What I especially like is the separation between knowing, trusting, and being authorized to execute a solution. That prevents the AI from becoming the authority over its own actions.

And I think many of the questions you listed can themselves become deterministic checks.

For example, before executing a known remediation, the system could compare the current environment against the evidence attached to the solution:

  • software and infrastructure versions

  • telemetry and problem signature

  • required preconditions

  • configuration/state differences

  • blast radius

  • rollback availability

  • policy scope

  • recent execution history

If those checks match strongly enough and policy permits it, deterministic automation can execute.

If the solution is known but the environment does not match closely enough, that becomes applicability uncertainty and the system can escalate to verification or AI reasoning.

If the remediation is technically valid but outside the permitted authority boundary, policy or a human decides.

I also strongly agree with the reverse lifecycle. Trust should decay when the environment changes, evidence becomes stale, or execution results start degrading.

So the registry becomes much more than storage. It becomes a governed operational memory/control plane where knowledge is continuously tested against reality.

The architecture I’m imagining is increasingly looking like:

Observe → Match known operational knowledge → Validate applicability → Check authority → Execute deterministic automation

and only when that chain cannot reach a safe decision:

AI investigates → proposes a new capability → evidence validates it → policy governs it → automation absorbs it

That is what makes the idea interesting to me: the system should continuously expand its deterministic capability without allowing AI reasoning itself to become an unchecked source of authority.