You would not let a new engineer ship production code without reviewing past failures, architectural docs, system metrics, or deployment mechanisms.
So why are your AI agents coding in the dark?
AI is already doing a third of the work in many engineering teams. Coding, testing, generating documentation, even proposing changes to infra and pipelines. The tools are getting better. The prompts are getting tighter. But the context is still missing.
Your GPT agents, your Claude workflows, your copilots and bots are all guessing at how your system really works.
Because they do not know what your system has already been through.
That is about to change.
COEhub is building a Machine Curated incident knowledgebase that captures the full context of incidents, systems and delivers and MCP server for integration with your AI chat tools and AI agents. This gives your AI agents the same access to incident intelligence that your humans already have.
This is not just a readout of tickets or a summary of alerts. It is a living graph of past failure, contributing factors, root causes and system behavior. And it is available to both humans and machines.
Imagine an AI agent writing infrastructure code.
Now imagine it knows that the last five times a certain config was changed, production fell over.
It knows the lead time to detect that class of failure.
It knows that a mitigation path exists but has never been automated.
Now the agent does not just write code. It writes safer code. More aligned code. Code that reflects the reality of how your system behaves under stress.
That is what happens when you plug AI into your org's operational memory.
Everyone is talking about hallucinations. About agents making dangerous changes. About AI suggesting patterns that do not work in real-world systems.
What if the solution is not more control?
What if the solution is better context?
An AI that learns from your incident history becomes better at assisting, better at debugging and better at shipping. It does not just autocomplete code. It completes your team's understanding of the system.
This is not some sci-fi agent. This is happening now. And if your AI tools are not hooked into a source of real failure data, they are not learning. They are just guessing faster.
COEhub already powers human learning. It ingests incidents, structures postmortems, builds timelines, detects patterns and surfaces blind spots. It helps teams get better with every failure.
The MCP endpoint turns that same system into an API that your tools can use.
It is like giving your agents access to a live SRE brain trained on everything your systems have ever done wrong.
All of them can ask COE what happened last time. And make smarter decisions in real time.
The next big leap in AI for engineering is not just better models. It is better memory.
The companies that win are the ones whose agents know more than just public code and textbook patterns. They know their own systems. Their own history. Their own pain.
The MCP endpoint is how you give your AI that edge.
Plug in. Let your AI learn from your past. Build systems that do not just move fast but get better with every incident.
COEhub is how you turn your failures into fuel. For people. And now for agents too.