Building an AI-Powered Incident Response Agent with Hindsight

keerthana k — Tue, 29 Sep 2026 17:40:49 +0000

Hack With Hyderabad 3.0 | AI Agents | DevOps | Hindsight Memory

🚨 When Production Breaks, What If Your AI Agent Could Remember?

Imagine this.

It's 2 AM.

A production service suddenly starts returning errors. The monitoring dashboard turns red. Users are affected, and the engineering team starts investigating.

Someone on the team says:

"I think we've seen this problem before."

But where?

Maybe the solution is buried inside an old incident report. Maybe someone documented it in a post-mortem. Maybe an engineer remembers the fix—but they're currently unavailable.

The problem isn't always that the team doesn't know how to solve the incident.

Sometimes the problem is remembering that the solution already exists.

That is the problem we wanted to explore with our AI Incident Response Agent.

💡 The Problem

Modern applications depend on many services:

APIs
Databases
Authentication systems
Cloud infrastructure
Deployment pipelines
External services
Message queues
Monitoring systems

When something fails, engineers need to quickly determine:

What happened?
Which service is affected?
What changed recently?
What could be causing the problem?
Has something similar happened before?
What worked previously?
What solutions failed?
What should we try next?

Traditional incident-response workflows often require engineers to search through previous incidents, tickets, logs, documentation, and runbooks.

The valuable knowledge from previous incidents can exist, but it isn't necessarily available at the exact moment it is needed.

We wanted to build a system that changes this.

🧠 Our Idea: An Incident Response Agent With Memory

Our project is an AI-powered Incident Response Agent that uses Hindsight as a persistent memory layer.

Instead of treating every incident as a completely new problem, the agent can use information from previous incidents to provide additional context during a new investigation.

The core idea is:

Remember → Recall → Analyze → Guide → Resolve → Learn

The goal isn't to replace engineers.

The goal is to give engineers an AI assistant that can bring relevant historical experience into the current incident.

🔄 How It Works

Our overall workflow looks like this:

                 🚨 INCIDENT
                      │
                      ▼
              🤖 AI AGENT
                      │
                      ▼
             📊 ANALYZE SIGNALS
                      │
                      ▼
             🧠 HINDSIGHT RECALL
                      │
                      ▼
          🔎 SIMILAR INCIDENTS
                      │
                      ▼
            💡 ROOT-CAUSE HINTS
                      │
                      ▼
          🛠️ RECOMMENDED ACTION
                      │
                      ▼
             👨‍💻 HUMAN REVIEW
                      │
                      ▼
                 ✅ RESOLVE
                      │
                      ▼
             🧠 RETAIN OUTCOME

This creates a continuous learning loop.

When an incident is resolved, the useful information from that investigation can become part of the agent's future memory.

Hindsight provides memory functionality through operations such as Retain, Recall, and Reflect.

🚨 Example: Payment API Incident

Let's take a simple example.

A production Payment API suddenly starts returning 503 errors.

The incident dashboard shows:

🔴 CRITICAL INCIDENT

Payment API
503 Service Unavailable

Affected Service:
Payment API

Current Error Rate:
31%

Database Connections:
98%

CPU:
92%

Recent Deployment:
Yes

The agent begins investigating the available signals.

It notices that database connections are unusually high.

But instead of immediately assuming that the database is the root cause, it asks another question:

Have we seen something similar before?

🧠 Hindsight Searches Previous Incidents

The agent sends the current incident context to the Hindsight memory layer.

Hindsight can retain information and later recall relevant memories based on a query. Its documentation describes memory banks as isolated containers for memories, while recall retrieves relevant stored information.

The system finds a previous incident with similar characteristics.

For example:

HISTORICAL INCIDENT

Incident ID:
INC-2026-087

Service:
Payment API

Symptoms:
• 503 errors
• High database connections
• Increased latency

Previous Investigation:
Database connection exhaustion

Previous Resolution:
Connection pool configuration was adjusted.

The historical incident doesn't automatically become the answer.

Instead, it becomes evidence that helps guide the current investigation.

This distinction is important.

A previous incident can suggest a direction, but the current system's evidence still needs to be considered.

🤖 What the AI Agent Does

The agent combines:

Current information

Error rates
CPU usage
Memory
Database connections
Recent deployments
Service health

Historical information

Similar incidents
Previous root causes
Previous fixes
Failed approaches
Successful resolutions
Relevant runbooks

It then produces an incident assessment.

For example:

AI INCIDENT ASSESSMENT

Likely Investigation Area:
Database connection exhaustion

Supporting Evidence:
✓ DB connections are critically high
✓ Payment API is returning 503 errors
✓ Similar historical incident found
✓ Previous incident involved connection exhaustion

Recommended Checks:

1. Inspect active DB connections
2. Compare connection pool configuration
3. Review the latest deployment
4. Check the database runbook

The important part is that the agent provides context and recommendations, rather than blindly executing production changes.

👨‍💻 Human-in-the-Loop

Production systems require caution.

An AI agent should not automatically perform every action simply because it believes an action is appropriate.

Our interface therefore includes a human review stage.

For example:

┌─────────────────────────────────────┐
│      RECOMMENDED ACTION             │
│                                     │
│ Scale Payment API                   │
│                                     │
│ Risk: MEDIUM                        │
│                                     │
│ Reason:                             │
│ Traffic is significantly above      │
│ the normal operating level.        │
│                                     │
│ [ APPROVE ACTION ]                  │
│ [ REJECT ]                          │
│ [ VIEW DETAILS ]                    │
└─────────────────────────────────────┘

For a potentially dangerous action, the system can instead require explicit approval.

This creates a balance:

AI provides speed and context.

Humans retain operational control.

🖥️ Our DevOps Dashboard

We designed the frontend as a professional incident-response command center rather than a traditional chatbot.

The dashboard contains several sections.

🏠 Command Center

The main screen provides an overview of current incidents.

ACTIVE INCIDENTS

🔴 CRITICAL
Payment API
503 Errors

🟠 HIGH
Authentication Service
High Latency

🟢 RESOLVED
Notification Service
Resolved

The goal is to give an engineer immediate visibility into the current operational situation.

🧠 Hindsight Memory

The Hindsight page makes the memory layer visible.

HINDSIGHT MEMORY

Searching historical incidents...

✓ 3 relevant memories found

INC-2026-087
Payment API Failure

Similarity:
High

Previous Root Cause:
Database Connection Exhaustion

Previous Resolution:
Connection Pool Adjustment

This helps demonstrate one of the central ideas behind the project:

The agent isn't only analyzing what is happening now.

It can also use what happened before.

🤖 AI Agent Activity

The agent page shows the investigation process at a high level.

INCIDENT RESPONSE AGENT

✓ Incident detected
✓ System signals collected
✓ Recent deployment checked
✓ Historical memory searched
✓ Similar incident found
● Generating recommendations

We intentionally present the agent's actions and evidence, rather than exposing private chain-of-thought reasoning.

📜 Incident Timeline

The timeline provides a chronological view of the response.

10:42:03  🚨 Incident detected

10:42:15  🤖 Agent started analysis

10:42:22  📊 DB connections reached critical level

10:42:31  🧠 Hindsight search started

10:42:35  🔎 Similar incident found

10:42:41  💡 Recommendation generated

10:43:02  👨‍💻 Engineer reviewed recommendation

10:43:18  ✅ Action approved

10:44:02  🟢 Error rate decreasing

This makes it easy to understand what happened during an incident.

📚 Runbooks

The system can also connect incident types with operational runbooks.

Example:

RUNBOOKS

🔧 API 503 Errors
🗄️ Database Connection Issues
🔐 Authentication Failure
☁️ Deployment Failure
🌐 Network Connectivity

When the agent identifies a likely incident category, it can point engineers toward the relevant procedure.

📊 Analytics

The dashboard also includes incident analytics such as:

Total incidents
Active incidents
Resolved incidents
Average response time
Incident categories
Historical matches
Frequently affected services

For the hackathon prototype, these values can come from our demonstration dataset.

We do not treat simulated dashboard numbers as proof of production performance.

🔁 The Learning Loop

One of the most important parts of our architecture happens after the incident is resolved.

The investigation shouldn't simply disappear.

Useful information from the incident can be retained:

Incident
   ↓
Investigation
   ↓
Root Cause
   ↓
Actions Taken
   ↓
Outcome
   ↓
🧠 Hindsight Retain
   ↓
Future Incident

When a similar problem happens later, the agent can recall that experience.

Hindsight is designed specifically around persistent agent memory and supports retaining information, recalling relevant memories, and reflecting over existing memories.

🏗️ High-Level Architecture

Our conceptual architecture looks like this:

┌─────────────────────────────┐
│      Monitoring / Alerts    │
└──────────────┬──────────────┘
               │
               ▼
┌─────────────────────────────┐
│    Incident Response Agent  │
│                             │
│  • Incident analysis        │
│  • Signal interpretation    │
│  • Recommendation generation│
└──────────────┬──────────────┘
               │
       ┌───────┴────────┐
       ▼                ▼
┌─────────────┐  ┌───────────────┐
│ Current     │  │   Hindsight   │
│ Incident    │  │    Memory     │
│ Data        │  │               │
└─────────────┘  └───────┬───────┘
                         │
                         ▼
                  Historical Context
                         │
                         ▼
                ┌────────────────┐
                │ Recommendation │
                └───────┬────────┘
                        │
                        ▼
                👨‍💻 Human Review
                        │
                        ▼
                     Action
                        │
                        ▼
                🧠 Retain Outcome

🔐 Why Memory Matters

An ordinary AI agent can analyze the information you give it.

But an incident-response agent becomes more useful when it can also access relevant experience from previous incidents.

Consider these two situations.

Agent A

New incident received.

Analyze current information.
Start investigation from scratch.

Agent B

New incident received.

Analyze current information.
Search historical incidents.
Find similar incident.
Review previous outcome.
Use that context to guide investigation.

The second approach gives the agent another source of context.

That is the main idea behind our project.

🛡️ Memory Should Not Mean Blind Trust

There is an important limitation.

Historical information can be outdated or incorrect.

Therefore:

Previous incidents should be treated as context, not absolute truth.

The current system state must still be checked.

For example, a previous incident may have been caused by database overload, while the current incident may have a completely different cause.

The agent should therefore use memory to guide investigation, not automatically determine the answer.

Our goal is not to replace the engineer.

It is to make the engineer's next incident investigation more informed by the team's previous experience.

Built For

Hack With Hyderabad 3.0

Project: AI Incident Response Agent

Core Technology: Hindsight Memory

Focus Areas: AI Agents • DevOps • Incident Response • Persistent Memory

DEV Community: keerthana k

Building an AI-Powered Incident Response Agent with Hindsight

🚨 When Production Breaks, What If Your AI Agent Could Remember?

💡 The Problem

🧠 Our Idea: An Incident Response Agent With Memory

🔄 How It Works

🚨 Example: Payment API Incident

🧠 Hindsight Searches Previous Incidents

🤖 What the AI Agent Does

Current information

Historical information

👨‍💻 Human-in-the-Loop

🖥️ Our DevOps Dashboard

🏠 Command Center

🧠 Hindsight Memory

🤖 AI Agent Activity

📜 Incident Timeline

📚 Runbooks

📊 Analytics

🔁 The Learning Loop

🏗️ High-Level Architecture

🔐 Why Memory Matters

Agent A

Agent B

🛡️ Memory Should Not Mean Blind Trust

Built For