Production incidents are rarely completely new.
A similar combination of high latency, resource exhaustion, configuration changes, or deployment issues may have happened before. However, a typical AI assistant analyzing an incident does not automatically remember what happened during previous incidents.
We built Incident Memory Agent to address this problem.
It is an AI-powered incident-response assistant that uses Hindsight persistent memory to recall previous production incidents, analyze new incidents using historical context, retain new experiences, and reflect on recurring patterns.
The central idea is:
AI should have not only intelligence, but also experience.
A stateless incident-response assistant can analyze the information provided in the current incident, but it may not have access to the organization's previous incident experience.
That means every incident can effectively become a new problem.
For example, imagine a production incident with:
If the organization previously experienced a similar incident and discovered that a cache invalidation bug caused the problem, that historical experience can be extremely valuable.
The challenge is making that experience available to the AI when the next incident occurs.
We built Incident Memory Agent, an AI incident-response system with persistent memory.
The system follows a continuous learning loop:
Recall → Analyze → Retain → Reflect
When a new incident is submitted, the agent first queries Hindsight for relevant historical memories.
These memories can include:
The AI then analyzes the current incident together with the historical context retrieved from Hindsight.
This allows the model to reason from both:
Current incident + Previous experience
rather than analyzing the incident in isolation.
After analyzing the incident, the new incident and its outcome are stored back into Hindsight.
This means the current incident becomes part of the agent's future experience.
Finally, Hindsight's reflection capability is used to identify higher-level patterns from the accumulated experience.
This helps move from individual incident memories toward reusable operational knowledge.
Consider a previous incident, INC-001.
The system remembered that:
Later, a new incident, INC-002, occurs:
The Incident Memory Agent recalls the previous experience from Hindsight.
Instead of starting from zero, the agent can use the historical evidence to identify the similarity and surface the previously successful resolution.
This is the behavior we wanted to demonstrate:
The agent learns from what happened before.
Hindsight is the core memory layer of our application.
Our application uses Hindsight for three important operations:
Stores incident experiences and outcomes in persistent memory.
Retrieves relevant historical memories when a new incident is analyzed.
Synthesizes recurring patterns from accumulated experiences.
This creates a feedback loop:
text
Current Incident
↓
RECALL
↓
Historical Experience
↓
AI Analysis
↓
RETAIN
↓
New Experience
↓
REFLECT
↓
Learned Pattern