DEV Community: Mārtiņš Veiss

Fleet Management with Ansible — The AutoBot Approach

Mārtiņš Veiss — Wed, 08 Apr 2026 19:12:50 +0000

Fleet Management with Ansible — The AutoBot Approach

Part 3: Scaling to Enterprise Infrastructure

You've completed Parts 1 and 2. You're running AutoBot, your knowledge base is populated, and you're comfortable with the basics. Now comes the hard part: scaling your infrastructure to dozens of servers across multiple data centers.

Managing 10 servers is manageable with SSH and scripts. Managing 50 servers? That's painful. Managing 100+? That's impossible without orchestration.

The problems multiply: manual deployment coordination across regions, unpredictable rollback times, team members overwriting each other's changes, onboarding new engineers who don't know your procedures, configuration drift creeping in over weeks. You need something that treats your entire fleet as a cohesive unit—something that can deploy a change, verify health across all servers, and roll back if anything fails.

Enter AutoBot + Ansible. Together, they solve the orchestration challenge. Ansible has the power. AutoBot adds intelligence, discoverability, and real-time coordination. This post shows you the complete enterprise approach.

Ansible Basics: Quick Recap

If you've followed Part 1, you know Ansible is an agentless configuration management tool. You define infrastructure state in playbooks (YAML files describing tasks), organize them into roles (reusable logic), and target servers with inventories (server lists grouped by function).

A simple playbook looks like:

- hosts: webservers
  tasks:
    - name: Deploy app
      command: /opt/deploy/restart-app.sh

Traditional Ansible is powerful but has friction: you SSH into a bastion host, run playbook commands, monitor output, troubleshoot manually. At scale, this becomes a bottleneck.

AutoBot extends Ansible by making playbooks discoverable through natural language, orchestrating complex multi-step workflows automatically, adding pre-deployment health checks, providing real-time status updates, and enabling intelligent rollback decisions based on actual health metrics—not just task completion.

AutoBot + Ansible Architecture

Here's how AutoBot elevates Ansible to enterprise scale:

┌─────────────────────────────────────────────────────────┐
│ Chat Command: "Deploy v2.5 to production"               │
└─────────────┬───────────────────────────────────────────┘
              ↓
    ┌─────────────────────┐
    │ Parse & Intent      │
    │ Determine target    │
    │ Validate access     │
    └────────┬────────────┘
             ↓
  ┌──────────────────────────────────────┐
  │ AutoBot Fleet Orchestrator           │
  │ - Selects matching playbooks         │
  │ - Orders execution by dependency     │
  │ - Determines parallel vs serial      │
  └──────────┬───────────────────────────┘
             ↓
  ┌──────────────────────────────────────────────────┐
  │ Ansible Inventory & Playbooks                    │
  │ (50+ production servers across 5 data centers)   │
  └──────────┬───────────────────────────────────────┘
             ↓
  ┌────────────────────────────────────────────────────┐
  │ Parallel Execution Layer                           │
  │ - Pre-deployment checks (disk, service health)    │
  │ - Rolling deployment (batches)                    │
  │ - Health verification after each batch            │
  │ - Automatic rollback on failure                   │
  └────────────┬─────────────────────────────────────┘
               ↓
  ┌─────────────────────────────────────────────────┐
  │ Real-time Monitoring & Reporting                │
  │ ✓ 50/50 servers deployed successfully           │
  │ ✓ Health checks: All green                       │
  │ ✓ Deployment complete: 12 minutes                │
  └─────────────────────────────────────────────────┘

The flow: Chat command → intent parsing → playbook selection → dependency orchestration → parallel execution with rolling strategy → health checks at each stage → real-time status updates → completion report.

Deep Example: Zero-Downtime Production Deployment

Scenario: Deploy a critical service update (v2.5) to 50+ production servers across 5 data centers. Traditional approach: 2-3 hours of manual work, SSH sessions to each region, testing at each step, risk of human error.

With AutoBot + Ansible: 15 minutes, completely orchestrated.

ansible-playbook deploy-v2.5.yml \
  --inventory production-inventory.ini \
  --limit "webservers:&us-east" \
  --extra-vars "batch_size=10 health_check=true rollback_on_failure=true" \
  --tags "pre-check,deploy,validate"

Step 1: Pre-deployment Checks (2 minutes)
AutoBot runs checks across all 50 servers in parallel:

Verify 20% free disk space on /opt/app
Confirm core services are healthy
Validate database connectivity from each app server
Check load balancer is accessible

If any server fails, deployment stops and reports the issue before touching production.

Step 2: Rolling Deployment (10 minutes)
Deploy in batches of 10 servers, removing from load balancer before deployment:

Remove 10 servers from load balancer
Deploy v2.5 binary (~1 minute per batch, parallelized)
Run post-deploy smoke test (curl endpoints, verify response codes)
Restore to load balancer
Wait 30 seconds for traffic to normalize
Repeat for next batch

During this process, 40 servers continue serving traffic. User impact: zero. The load balancer handles traffic gracefully across remaining capacity.

Step 3: Canary Validation (1 minute)
Before declaring success, AutoBot validates:

Error rate on newly deployed servers < baseline
Response latency within acceptable bounds
No spike in database queries per server
Health check endpoints return 200

Step 4: Rollback Capability (available immediately)
If any metric fails validation, AutoBot automatically:

Stops further deployments
Rolls back deployed servers to previous version
Restores original traffic distribution
Alerts on-call team with detailed logs

Real performance: 50 servers, 100MB binary deployment ≈ 1 minute network transfer (bandwidth-limited), 2-3 minutes per batch at current scale.

Advanced Features

Health Checks & Intelligent Pausing

AutoBot monitors health during deployment. If a health check fails on any batch:

- name: Post-deploy health check
  uri:
    url: http://localhost:8080/health
    method: GET
  register: health
  failed_when: health.status != 200

Deployment pauses. AutoBot provides context: "Batch 3 (us-west-2) failed health checks. Error rate spiked from 0.1% to 2.5%. Rollback batch 3? [Y/n]" You investigate, fix the issue, resume without redeploying unaffected servers.

Conditional Deployments

Some services have dependencies. Deploy cache service before application layer before API gateway:

- name: Deploy cache tier
  hosts: cache_servers
  tags: [cache]

- name: Deploy app tier
  hosts: app_servers
  tags: [app]
  dependencies: [cache]

- name: Deploy API gateway
  hosts: api_gateway
  tags: [gateway]
  dependencies: [app]

AutoBot respects dependency order, parallelizing independent paths. Cache and database upgrades run in parallel. Application waits for both. Gateway waits for application.

Real-time Status in Chat

You: Deploy cache-v3 to production
AutoBot: Starting deployment to 15 cache servers...
  ✓ Pre-checks passed
  • Batch 1: Deploying (3/5 servers done)
  • Batch 2: Queued
  ✓ Health: All green
  ETA: 6 minutes

No SSH. No log tailing. Just clear, real-time progress in your chat interface.

Performance & Scale

Fleet size: Tested to 500+ servers. Response time under 30 seconds to start orchestration, sub-second status queries.

Deployment speed: Network bandwidth is the limiting factor. A 100MB binary across 50 servers ≈ 1 minute (assuming 10 Gbps cluster network). Configuration changes without binary transfer ≈ 20 seconds.

Failure handling: Detect failure on one server, pause orchestration, investigate, resume remaining batches without redeploying successful servers. Zero re-work.

Optimization: Choose rolling deployments for critical services (maintain capacity), canary for lower-risk changes (faster feedback), or blue-green for instant rollback on database schema changes.

Closing

You've now completed the full AutoBot trilogy:

Part 1: Building a Self-Hosted AI Platform — Get AutoBot running, understand the chat interface, manage your first fleet.

Part 2: How We Use RAG for Knowledge Base Search — Turn your scattered runbooks into instant, intelligent answers.

Part 3: Fleet Management with Ansible — Orchestrate enterprise infrastructure with zero-downtime deployments and intelligent health management.

Deploy your first fleet. Join the community. Infrastructure automation is no longer a luxury—it's essential for scale.

What's your biggest orchestration challenge? Let me know in the comments.

Fleet Management with Ansible — The AutoBot Approach

Mārtiņš Veiss — Wed, 08 Apr 2026 14:19:48 +0000

Fleet Management with Ansible — The AutoBot Approach

Part 3: Scaling to Enterprise Infrastructure

Managing 10 servers is manageable with SSH and scripts. Managing 50 servers? That's painful. Managing 100+? That's impossible without orchestration.

Ansible Basics: Quick Recap

A simple playbook looks like:

- hosts: webservers
  tasks:
    - name: Deploy app
      command: /opt/deploy/restart-app.sh

Traditional Ansible is powerful but has friction: you SSH into a bastion host, run playbook commands, monitor output, troubleshoot manually. At scale, this becomes a bottleneck.

AutoBot + Ansible Architecture

Here's how AutoBot elevates Ansible to enterprise scale:

┌─────────────────────────────────────────────────────────┐
│ Chat Command: "Deploy v2.5 to production"               │
└─────────────┬───────────────────────────────────────────┘
              ↓
    ┌─────────────────────┐
    │ Parse & Intent      │
    │ Determine target    │
    │ Validate access     │
    └────────┬────────────┘
             ↓
  ┌──────────────────────────────────────┐
  │ AutoBot Fleet Orchestrator           │
  │ - Selects matching playbooks         │
  │ - Orders execution by dependency     │
  │ - Determines parallel vs serial      │
  └──────────┬───────────────────────────┘
             ↓
  ┌──────────────────────────────────────────────────┐
  │ Ansible Inventory & Playbooks                    │
  │ (50+ production servers across 5 data centers)   │
  └──────────┬───────────────────────────────────────┘
             ↓
  ┌────────────────────────────────────────────────────┐
  │ Parallel Execution Layer                           │
  │ - Pre-deployment checks (disk, service health)    │
  │ - Rolling deployment (batches)                    │
  │ - Health verification after each batch            │
  │ - Automatic rollback on failure                   │
  └────────────┬─────────────────────────────────────┘
               ↓
  ┌─────────────────────────────────────────────────┐
  │ Real-time Monitoring & Reporting                │
  │ ✓ 50/50 servers deployed successfully           │
  │ ✓ Health checks: All green                       │
  │ ✓ Deployment complete: 12 minutes                │
  └─────────────────────────────────────────────────┘

Deep Example: Zero-Downtime Production Deployment

With AutoBot + Ansible: 15 minutes, completely orchestrated.

ansible-playbook deploy-v2.5.yml \
  --inventory production-inventory.ini \
  --limit "webservers:&us-east" \
  --extra-vars "batch_size=10 health_check=true rollback_on_failure=true" \
  --tags "pre-check,deploy,validate"

Step 1: Pre-deployment Checks (2 minutes)
AutoBot runs checks across all 50 servers in parallel:

Verify 20% free disk space on /opt/app
Confirm core services are healthy
Validate database connectivity from each app server
Check load balancer is accessible

If any server fails, deployment stops and reports the issue before touching production.

Step 2: Rolling Deployment (10 minutes)
Deploy in batches of 10 servers, removing from load balancer before deployment:

Remove 10 servers from load balancer
Deploy v2.5 binary (~1 minute per batch, parallelized)
Run post-deploy smoke test (curl endpoints, verify response codes)
Restore to load balancer
Wait 30 seconds for traffic to normalize
Repeat for next batch

During this process, 40 servers continue serving traffic. User impact: zero. The load balancer handles traffic gracefully across remaining capacity.

Step 3: Canary Validation (1 minute)
Before declaring success, AutoBot validates:

Error rate on newly deployed servers < baseline
Response latency within acceptable bounds
No spike in database queries per server
Health check endpoints return 200

Step 4: Rollback Capability (available immediately)
If any metric fails validation, AutoBot automatically:

Stops further deployments
Rolls back deployed servers to previous version
Restores original traffic distribution
Alerts on-call team with detailed logs

Real performance: 50 servers, 100MB binary deployment ≈ 1 minute network transfer (bandwidth-limited), 2-3 minutes per batch at current scale.

Advanced Features

Health Checks & Intelligent Pausing

AutoBot monitors health during deployment. If a health check fails on any batch:

- name: Post-deploy health check
  uri:
    url: http://localhost:8080/health
    method: GET
  register: health
  failed_when: health.status != 200

Conditional Deployments

Some services have dependencies. Deploy cache service before application layer before API gateway:

- name: Deploy cache tier
  hosts: cache_servers
  tags: [cache]

- name: Deploy app tier
  hosts: app_servers
  tags: [app]
  dependencies: [cache]

- name: Deploy API gateway
  hosts: api_gateway
  tags: [gateway]
  dependencies: [app]

AutoBot respects dependency order, parallelizing independent paths. Cache and database upgrades run in parallel. Application waits for both. Gateway waits for application.

Real-time Status in Chat

You: Deploy cache-v3 to production
AutoBot: Starting deployment to 15 cache servers...
  ✓ Pre-checks passed
  • Batch 1: Deploying (3/5 servers done)
  • Batch 2: Queued
  ✓ Health: All green
  ETA: 6 minutes

No SSH. No log tailing. Just clear, real-time progress in your chat interface.

Performance & Scale

Fleet size: Tested to 500+ servers. Response time under 30 seconds to start orchestration, sub-second status queries.

Failure handling: Detect failure on one server, pause orchestration, investigate, resume remaining batches without redeploying successful servers. Zero re-work.

Optimization: Choose rolling deployments for critical services (maintain capacity), canary for lower-risk changes (faster feedback), or blue-green for instant rollback on database schema changes.

Closing

You've now completed the full AutoBot trilogy:

Part 1: Building a Self-Hosted AI Platform — Get AutoBot running, understand the chat interface, manage your first fleet.

Part 2: How We Use RAG for Knowledge Base Search — Turn your scattered runbooks into instant, intelligent answers.

Part 3: Fleet Management with Ansible — Orchestrate enterprise infrastructure with zero-downtime deployments and intelligent health management.

Deploy your first fleet. Join the community. Infrastructure automation is no longer a luxury—it's essential for scale.

What's your biggest orchestration challenge? Let me know in the comments.

How We Use RAG for Knowledge Base Search in AutoBot

Mārtiņš Veiss — Wed, 08 Apr 2026 14:14:38 +0000

How We Use RAG for Knowledge Base Search in AutoBot

Part 2: Unlocking Your Team's Collective Intelligence

In Part 1, you set up AutoBot and experienced how it can execute basic infrastructure tasks. Now let's unlock its real power: turning your scattered knowledge into instant, intelligent answers.

Where does your team's critical knowledge live? Deployment runbooks in Google Drive. Database failover procedures in forgotten Confluence docs. Incident post-mortems buried in Slack. At 3 AM during an outage, finding that knowledge is nearly impossible.

AutoBot solves this with Retrieval-Augmented Generation (RAG)—a technique that lets AutoBot search your actual documentation and generate answers based on your procedures, not generic training data. We'll explore how RAG works, build a practical knowledge base, and show you why this beats traditional keyword search.

What Is RAG? (Plain English)

RAG stands for Retrieval-Augmented Generation—three operations in one:

Retrieval: Find relevant documents
Augmented: Enhance the AI's answer with those documents
Generation: LLM writes the final answer

RAG answers questions using your knowledge, not the LLM's training data.

Example: You ask AutoBot: "How do we handle database replication lag?"

Without RAG, the LLM guesses with generic textbook advice. With RAG:

AutoBot searches your knowledge base (runbooks, procedures, incidents)
Finds documents about your team's replication remediation steps
Generates an answer grounded in your procedures
You get: "Based on your runbook, first check replication status with SHOW REPLICA STATUS, then..."

Generic advice versus actionable, organization-specific answers. That's why RAG is a game-changer for infrastructure knowledge management.

How AutoBot + RAG Works: The Technical Flow

Let's walk through how AutoBot transforms your documents into searchable intelligence.

┌────────────────────────────────────────────────────┐
│           AutoBot RAG Pipeline                      │
├────────────────────────────────────────────────────┤
│                                                    │
│  1. DOCUMENTS                                      │
│     (Runbooks, Procedures, Incidents)             │
│              ↓                                      │
│  2. VECTORIZATION                                  │
│     Convert text → mathematical vectors           │
│     (Embeddings capture meaning)                  │
│              ↓                                      │
│  3. STORAGE                                        │
│     Save vectors in database (ChromaDB)           │
│     With original text for reference              │
│              ↓                                      │
│  ════════════════════════════════════════          │
│              (Knowledge Base Ready)                │
│  ════════════════════════════════════════          │
│              ↓                                      │
│  4. USER QUERY                                     │
│     "How do we handle X?"                         │
│              ↓                                      │
│  5. QUERY VECTORIZATION                            │
│     Convert question → vector                     │
│              ↓                                      │
│  6. SIMILARITY SEARCH                              │
│     Find most similar document vectors            │
│              ↓                                      │
│  7. RETRIEVAL                                      │
│     Extract relevant document chunks              │
│              ↓                                      │
│  8. GENERATION                                     │
│     LLM reads docs + generates answer             │
│              ↓                                      │
│  ANSWER (grounded in YOUR knowledge)              │
│                                                    │
└────────────────────────────────────────────────────┘

Why embeddings beat keyword search: Keyword search looks for exact word matches and fails when terminology differs. Embeddings capture meaning—they understand "lag," "slowness," and "delays" are related. They find the right document even with different wording.

Vector databases store embeddings efficiently for sub-second retrieval even at massive scale. When your question arrives, AutoBot converts it to the same vector space and finds the closest neighbors—your most relevant documents.

Building Your First Knowledge Base: A Practical Walkthrough

Let's get hands-on. Here's how you build a RAG-powered knowledge base in AutoBot.

Step 1: Prepare Your Documents

Gather your source material. For our example, let's use a deployment runbook:

# Database Failover Runbook

## Quick Reference
- RTO: 5 minutes
- RPO: 0 (synchronous replication)

## Detection
1. Check replication lag: SHOW REPLICA STATUS
2. If lag > 10 seconds, investigate primary
3. Monitor replica_lag_ms metric in Prometheus

## Failover Process
1. Verify replica is caught up (Seconds_Behind_Master = 0)
2. Run: STOP SLAVE; CHANGE MASTER TO MASTER_HOST='new_primary'
3. Validate data integrity with pt-table-checksum
4. Update connection strings in config management
5. Run deployment hook to restart services

Step 2: Upload to AutoBot

Open AutoBot's chat interface:

You: Upload database-failover-runbook.md to my knowledge base
AutoBot: ✓ Indexed 1,847 tokens from database-failover-runbook.md
         Document ID: kb_database_failover_001
         Ready for queries

Step 3: Test with a Query

You: Our database replica is running 30 seconds behind. What should we do?
AutoBot: Based on your Database Failover Runbook, your target lag is 
         < 10 seconds. Current lag of 30s indicates a problem. 

         Immediate steps:
         1. Check if replica query is slow: SHOW PROCESSLIST
         2. Look for long-running queries blocking replication
         3. Monitor replica_lag_ms in Prometheus for trends

         If lag doesn't improve in 5 minutes, escalate to consider failover
         per your documented RTO of 5 minutes.

Step 4: Build Your Library

Repeat for each major area:

Deployment procedures
Incident response playbooks
Network troubleshooting guides
Capacity planning thresholds
On-call escalation procedures

Pro Tips for Best Results:

One topic per document: Keep deployment separate from scaling separate from incident response
Use clear headers: AutoBot chunks by sections—descriptive headers improve retrieval
Include context: Add scope like "This applies to production MySQL 5.7+"
Update regularly: AutoBot re-indexes when you update documents
Add decision logic: For troubleshooting, explicit decision trees help RAG pick the right path

Real Scenario: 3 AM Production Incident

This happened to us last month. 2:47 AM: Database replication lag alert fires.

Without RAG:

Dig through Google Drive for database runbook (3 minutes)
Find conflicting procedures in Confluence (2 minutes, confused)
Call groggy database lead (5 minutes)
Execute unsurely: 15 minutes elapsed

With AutoBot RAG:

On-call: "AutoBot, show me our database failover procedure"
AutoBot returns exact current runbook instantly
Execute with confidence: 5 minutes total

A 10-minute difference is the gap between contained incident and data corruption spreading. RAG delivers: when you're stressed and the clock is ticking, your team's collective wisdom is one question away.

Performance & Best Practices

Common Questions We Hear:

How many documents can AutoBot handle?
Thousands. We've tested with 10,000+ documents. Response time stays under 5 seconds even at scale.

What about response latency?
Query vectorization + retrieval + generation = < 5 seconds typically. Most of that is LLM generation time, not RAG overhead.

How do I keep knowledge accurate?
Update your source documents—AutoBot automatically re-indexes when you upload new versions. Treat your knowledge base like code: versioned, reviewed, maintained.

What formats are supported?
Markdown, plain text, and PDF. We recommend Markdown for best semantic chunking.

One more pro tip:
Organize by functional area. Don't dump everything into one mega-document. "Deployment" should be separate from "Scaling" from "Incident Response." Better documents = better retrieval = better answers.

What's Next?

You've now seen how AutoBot turns your scattered knowledge into instant, intelligent answers. But infrastructure management is more than just knowledge—it's about orchestration at scale.

In Part 3: Fleet Management with Ansible, we'll show you how AutoBot coordinates across your entire infrastructure—deploying to thousands of servers, managing configuration drift, and orchestrating complex multi-step deployments.

Ready to scale? Let's go.

Building a Self-Hosted AI Platform with AutoBot

Mārtiņš Veiss — Wed, 08 Apr 2026 14:14:35 +0000

Building a Self-Hosted AI Platform with AutoBot

Hook: The 30% Problem

You spend roughly 30% of your day on repetitive infrastructure tasks. SSH-ing into servers to check logs. Writing deployment commands across environments. Hunting through documentation when things break. Most of it's routine work that should be automated.

The problem isn't lacking tools—you have Terraform, Ansible, Docker. The problem is context-switching. You leave the command line, dive into config files, debug YAML, then come back. It's inefficient and it adds mental overhead.

What if you could talk to your infrastructure like a colleague? Ask questions, trigger deployments, check system health—all from one conversational interface. That's AutoBot.

By the end of this post, you'll understand what AutoBot is and have it running in under 5 minutes.

What Is AutoBot?

AutoBot is a self-hosted AI platform for infrastructure automation. Everything runs on your hardware, not in someone else's cloud. Your data stays yours. Your configuration stays in your control. No external API calls. No vendor lock-in.

Why does self-hosted matter? When you upload infrastructure secrets, runbooks, and procedures to a cloud service, that data lives on someone else's servers. It travels over networks you don't control. It's processed by machine learning models you didn't train. Self-hosted flips this: your infrastructure knowledge lives in your private network.

For compliance-heavy industries (healthcare, finance, government), this is mandatory. For everyone else, it's peace of mind. For cost-sensitive organizations, no SaaS bills or egress fees.

One dashboard. Your infrastructure. Complete control.

AutoBot complements your existing tools rather than replacing them. You still use Terraform for infrastructure-as-code and Ansible for configuration management. AutoBot becomes the conversational layer that ties everything together and reduces friction in day-to-day operations.

Key Features Explained

Chat Interface: Talk to Your Infrastructure

Imagine asking your infrastructure a question and getting an answer. "What's happening on the production servers right now?" "Deploy the latest version of the API." "Find all processes using more than 80% CPU." These aren't fantasy—they're natural language commands that AutoBot executes through its chat interface.

The chat interface is a conversational AI endpoint that understands infrastructure language. You type a question or command, AutoBot parses your intent, executes the appropriate action, and returns results. For example:

You: "How many containers are running?"
AutoBot: "I found 14 containers across your fleet. 12 are in 'running' state, 2 are in 'exited' state."

This eliminates the need to memorize CLI commands or switch between tools.

Fleet Management: From 1 Server to 100

Managing a single server is straightforward. Managing 50 servers across three data centers gets complicated fast. Fleet management in AutoBot lets you treat your entire infrastructure as one logical unit.

Say you want to check disk usage across your fleet. Instead of SSH-ing into each server individually, you ask AutoBot: "Show me disk usage on all production servers." AutoBot fans out the request, collects responses, and presents a unified view. This scales from managing your homelab (1-2 servers) to enterprise infrastructure (100+ servers) without changing how you interact with your systems.

You: "Restart the web tier"
AutoBot: "Restarting 5 web servers... [progress updates] ✓ All 5 restarted successfully in 2m 15s"

Knowledge Bases: Your Runbooks as a Q&A Engine

Your team's knowledge is scattered. Runbooks live in Confluence. Deployment procedures are in wikis. Scripts are in GitHub. When a crisis hits at 3 AM, you can't find anything.

AutoBot solves this with knowledge bases—a feature that uses AI search to turn your documentation into an intelligent Q&A system. The system indexes your runbooks, procedures, and guides, then uses AI to answer questions by retrieving the most relevant information. This is RAG (Retrieval-Augmented Generation)—your documentation becomes an intelligent Q&A system.

You: "How do we handle database failover?"
AutoBot: [Retrieves relevant runbook section] "According to your runbooks, the failover procedure is: 1) Promote replica, 2) Update DNS, 3) Verify replication. Full steps are in section 3.2 of your DBA runbook."

Vision Processing: Understanding Your Screenshots

Not every problem is text-based. Sometimes you need to analyze a dashboard screenshot, a log file with formatting, or a diagram. AutoBot can process images—analyze dashboards, read logs, understand architecture diagrams.

When something breaks, you can screenshot your monitoring dashboard and ask AutoBot to explain what you're seeing:

You: [Upload screenshot of Grafana dashboard with alarms]
AutoBot: "I see 3 critical alerts: High CPU on db-primary, Memory above 90% on cache-node-2, and high network error rate. Based on your runbooks, this suggests a cascading failure. Recommended action: Scale cache tier."

Workflows: Automation Codified

Not everything is a one-off question. Some operations are complex, multi-step procedures that should run reliably every time. AutoBot supports workflows—either visual, declarative pipelines or code-based automation that runs on triggers.

A workflow might be: "On each deployment, run tests → build Docker image → push to registry → update Kubernetes manifests → roll out to staging → verify health checks." You define it once, then trigger it conversationally:

You: "Deploy the payment service to staging"
AutoBot: [Executes your pre-defined deployment workflow] "✓ Tests passed. ✓ Image built and pushed. ✓ Staging deployment complete. Health checks: green."

Real-World Example: A DevOps Team's Day

Before AutoBot:
Sarah starts at 9 AM. First: check deployment status. She SSHs into the monitoring server, checks Prometheus and Grafana (20 minutes). Next: bug fix deployment. Clone repo, review code, run tests (30 minutes), build Docker image, push, update manifests, deploy (45 minutes). Then a production alert fires. SSH into servers, check logs, hunt through three wikis for the fix, apply it, monitor (1.5 hours). The day is constant context-switching. By 5 PM, she's exhausted and hasn't tackled planned infrastructure improvements.

After AutoBot:
Sarah opens AutoBot's chat. "Status of deployments from yesterday?" — 30 seconds. "Deploy the bug fix to staging" — her workflow runs automatically (5 minutes). A production alert fires. "What's happening on the database servers?" AutoBot retrieves logs and suggests: "Memory leak in cache service. Fix is in runbook section 4.1." She applies it and it rolls out (15 minutes). By noon, critical tasks are done. The afternoon is spent on real improvements instead of fighting fires.

The difference: 4 hours of reactive work becomes 45 minutes of focused work. You move from one manual command away from mistakes to having documented, reliable processes.

Getting Started: 3 Steps to Running AutoBot

AutoBot runs in Docker, which means installation is genuinely simple.

Step 1: Clone and configure

git clone https://github.com/AutoBot/AutoBot.git
cd AutoBot
cp .env.example .env
# Edit .env with your infrastructure details (optional, defaults work)

Step 2: Start the platform

docker-compose up -d

Expected output:

Creating network "autobot_default" with default driver
Creating autobot_postgres ... done
Creating autobot_redis ... done
Creating autobot_core ... done
Creating autobot_api ... done
Starting autobot_web ... done

Step 3: Open your browser

Open http://localhost:8080
You should see the AutoBot dashboard login screen.
Default credentials: admin / Check your .env file for the default admin password

That's it. You're running AutoBot. Total time: about 2-3 minutes.

From here, the next steps are:

Add your infrastructure — Register your servers or cloud account
Upload documentation — Add your runbooks to knowledge bases
Ask your first question — "What's running on my servers?"

For detailed setup (non-Docker, cloud deployment, security hardening), see the full documentation.

Common Questions

"Is AutoBot a replacement for Terraform/Ansible?"

No. Terraform defines infrastructure as code. Ansible manages configuration. AutoBot wraps around these tools as a conversational interface. You still use Terraform to provision resources and Ansible to configure them. AutoBot just makes it easier to interact with your infrastructure from day to day.

"What about data privacy? Does AutoBot send data to the cloud?"

Everything stays on your hardware. AutoBot doesn't make external API calls to process your infrastructure data. Conversations stay in your database. If you choose to use LLMs (large language models), you can run them locally using Ollama or route them through your own API gateway. Full privacy.

"How much does it cost?"

Free. AutoBot is open source (MIT license). No licensing fees, no usage-based pricing, no surprise bills. Host it on your existing hardware.

"Do I need to be a Linux expert?"

No. If you can run Docker and basic shell commands, you can use AutoBot. Complex tasks (Kubernetes, Ansible) benefit from experience, but that's true for infrastructure work generally.

What's Next?

You've got AutoBot running. You've seen how it reduces the friction in infrastructure work. The real power unlocks when you teach AutoBot about your specific infrastructure and processes.

In the next post, we'll dive deep into knowledge bases. We'll cover how to structure your runbooks, how AutoBot's AI finds the right information when you need it, and how to leverage RAG (Retrieval-Augmented Generation) to make your team's knowledge searchable and intelligent.

Ready to get more power?

Read Part 2: How We Use RAG for Knowledge Base Search →

Building a Self-Hosted AI Platform with AutoBot

Mārtiņš Veiss — Wed, 08 Apr 2026 14:05:47 +0000

Building a Self-Hosted AI Platform with AutoBot

Hook: The 30% Problem

What if you could talk to your infrastructure like a colleague? Ask questions, trigger deployments, check system health—all from one conversational interface. That's AutoBot.

By the end of this post, you'll understand what AutoBot is and have it running in under 5 minutes.

What Is AutoBot?

For compliance-heavy industries (healthcare, finance, government), this is mandatory. For everyone else, it's peace of mind. For cost-sensitive organizations, no SaaS bills or egress fees.

One dashboard. Your infrastructure. Complete control.

Key Features Explained

Chat Interface: Talk to Your Infrastructure

You: "How many containers are running?"
AutoBot: "I found 14 containers across your fleet. 12 are in 'running' state, 2 are in 'exited' state."

This eliminates the need to memorize CLI commands or switch between tools.

Fleet Management: From 1 Server to 100

You: "Restart the web tier"
AutoBot: "Restarting 5 web servers... [progress updates] ✓ All 5 restarted successfully in 2m 15s"

Knowledge Bases: Your Runbooks as a Q&A Engine

Your team's knowledge is scattered. Runbooks live in Confluence. Deployment procedures are in wikis. Scripts are in GitHub. When a crisis hits at 3 AM, you can't find anything.

You: "How do we handle database failover?"
AutoBot: [Retrieves relevant runbook section] "According to your runbooks, the failover procedure is: 1) Promote replica, 2) Update DNS, 3) Verify replication. Full steps are in section 3.2 of your DBA runbook."

Vision Processing: Understanding Your Screenshots

When something breaks, you can screenshot your monitoring dashboard and ask AutoBot to explain what you're seeing:

You: [Upload screenshot of Grafana dashboard with alarms]
AutoBot: "I see 3 critical alerts: High CPU on db-primary, Memory above 90% on cache-node-2, and high network error rate. Based on your runbooks, this suggests a cascading failure. Recommended action: Scale cache tier."

Workflows: Automation Codified

You: "Deploy the payment service to staging"
AutoBot: [Executes your pre-defined deployment workflow] "✓ Tests passed. ✓ Image built and pushed. ✓ Staging deployment complete. Health checks: green."

Real-World Example: A DevOps Team's Day

The difference: 4 hours of reactive work becomes 45 minutes of focused work. You move from one manual command away from mistakes to having documented, reliable processes.

Getting Started: 3 Steps to Running AutoBot

AutoBot runs in Docker, which means installation is genuinely simple.

Step 1: Clone and configure

git clone https://github.com/AutoBot/AutoBot.git
cd AutoBot
cp .env.example .env
# Edit .env with your infrastructure details (optional, defaults work)

Step 2: Start the platform

docker-compose up -d

Expected output:

Creating network "autobot_default" with default driver
Creating autobot_postgres ... done
Creating autobot_redis ... done
Creating autobot_core ... done
Creating autobot_api ... done
Starting autobot_web ... done

Step 3: Open your browser

Open http://localhost:8080
You should see the AutoBot dashboard login screen.
Default credentials: admin / Check your .env file for the default admin password

That's it. You're running AutoBot. Total time: about 2-3 minutes.

From here, the next steps are:

Add your infrastructure — Register your servers or cloud account
Upload documentation — Add your runbooks to knowledge bases
Ask your first question — "What's running on my servers?"

For detailed setup (non-Docker, cloud deployment, security hardening), see the full documentation.

Common Questions

"How much does it cost?"

Free. AutoBot is open source (MIT license). No licensing fees, no usage-based pricing, no surprise bills. Host it on your existing hardware.

What's Next?

You've got AutoBot running. You've seen how it reduces the friction in infrastructure work. The real power unlocks when you teach AutoBot about your specific infrastructure and processes.

Ready to get more power?

Read Part 2: How We Use RAG for Knowledge Base Search →