AI SRE Agent for production incidents and on-call

Using DrDroid, every engineer on your team debugs like your best one.

How DrDroid can help engineers on call and during production incidents

How an AI SRE could help you with moving from firefighting to building resilience

Investigations

  • Any engineer can run a senior level investigation.

Proactive Checks

  • Define complex alerts in natural language

Alert Intelligence

  • Auto-classification and grouping of your alerts into incidents

Knowledge Transfer

  • Centralised Tribal knowledge and company context

Cost Intelligence

  • Run cost & security analysis on your infrastructure

Monitoring Health

  • Continuously improve your observability stack

Today, only your most experienced engineers know which logs to check, which service depends on what, and where to look when something breaks.

Because DrDroid already understands your full infrastructure — services, dependencies, deployments, and ownership — any engineer can ask a question and get an answer with the depth and context of your best SRE.

ALERT: order-svc pods in CrashLoopBackOff (prod, us-east-1)

Investigating...
Agent investigation trail

  1. Checked pod status and events
    • 3/5 pods in CrashLoopBackOff — exit code 137 (OOMKilled), memory at limit 512Mi
  2. Checked memory usage trend
    • Memory growing linearly from 180Mi to 512Mi over ~8 min after startup — classic leak
  3. Checked recent deployments
    • order-svc v2.8.0 deployed 25 min ago via ArgoCD — previous v2.7.3 was stable
  4. Compared release diff (v2.7.3 → v2.8.0)
    • Found: added opentelemetry-sdk v1.28 + batch span processor with no memory bounds
  5. Confirmed root cause
    • OTel batch processor buffering unbounded spans — memory grows until OOMKilled
    • v2.7.3 had no OTel SDK — no memory issues. Rollback is safe, no schema changes.
  6. Root cause: opentelemetry-sdk v1.28 added in v2.8.0 — unbounded batch processor
    • Recommendation: Rollback to v2.7.3 (safe). Then re-deploy with maxQueueSize=2048 and maxExportBatchSize=512 configured on the span processor.

Silent failures slip through because they span multiple signals — no single metric threshold can catch them.

Write a check in plain English and schedule it on a cron. The agent correlates across metrics, logs, and cluster state to catch degradation patterns that individual alerts would miss.

Step 1 — Engineer creates a proactive check

Check: "k8s cluster node health"
"Check node CPU/memory pressure, pod eviction rates, disk I/O on etcd nodes, kubelet restart counts, and pending pods across all node pools. Flag if any node is silently degrading."
Scheduled: every 30 minutes

Too complex for a single alert
Requires checking node metrics, kubelet, etcd & pods together
Agent handles it instead

Step 2 — Agent runs the check every 30 minutes

✓ 9:00
✓ 9:30
✓ 10:00
✓ 10:30
✓ 11:00
! 11:30 Issue found
Agent catches silent degradation across multiple signals

  • node-pool-b silently degrading
  • Disk I/O latency 3x on etcd nodes + kubelet restarts trending up
  • 12 pods pending on node-4 + memory pressure at 87% (no alert set)

No single metric would trigger an alert — pattern across 5 signals
Team fixed it proactively before pods started crashing or workloads got disrupted.

Too many alerts — most are noise, and real issues get buried. Existing tools deduplicate but don't understand what's actually happening.

Because the agent knows your architecture — which services are related, what was recently deployed, who owns what — it groups alerts by actual root cause, suppresses noise it has learned to ignore, and escalates by real impact.

Tribal knowledge walks out the door every time a senior engineer leaves.

DrDroid captures your infrastructure context and investigation patterns in a persistent knowledge layer — so institutional knowledge lives in the system, not in people's heads. New hires are productive in weeks, not months.

Overprovisioned resources and idle infrastructure waste money — but finding them requires checking across clusters, clouds, and tools.
Because DrDroid maps your entire infrastructure, it can identify savings holistically — from right-sizing pods to cleaning up unused resources across providers.

Cost Optimization Report

  • Monthly savings found: $4,280
  • Recommendations: 12
  • Resources analyzed: 847
  • Right-size 4 over-provisioned EC2 instances -$1,840/mo
  • Remove 3 unused EBS volumes (90+ days idle) -$960/mo
  • Switch 2 RDS instances to reserved pricing -$1,480/mo

Dashboards and alerts go stale as infrastructure evolves — new services ship without monitoring, old alerts fire for things that no longer exist.

The agent knows what's actually running and what's being monitored. It flags gaps, retires stale alerts, and suggests coverage for new services — keeping your observability aligned with your real infrastructure.

What makes DrDroid different

Your infrastructure, fully mapped — before the first investigation

DrDroid maps your tools, code, and infrastructure into a unified context graph — so agents answer questions the way your best engineers would.

Code & Application Knowledge Graph

Order-service

  • Python / FastAPI
  • REST API, gRPC
    Payment-service
  • Go / gRPC
  • Stripe, webhooks
    Auth-service
  • Node / Express
  • OAuth, JWT
  • Calls via gRPC
  • Validates token

80+ MCP servers custom built for oncall and production incidents

Connect DrDroid to 80+ predefined MCP servers, from SSH on remote servers to Kubernetes to APM tools or your own MCP servers.

Watch how DrDroid investigates complex production incidents

DrDroid does a complex trace analysis & log correlation
DrDroid identifies a cascading failure behind a performance issue
DrDroid finds reason for memory spike causing CrashLoopBackOff
Investigating issue in custom data pipeline
How AI translates Tribal Knowledge to Agentic Memory
How DrDroid identifies critical issues amongst noisy alerts
AI does a deep dive of infrastructure costs and finds efficiency
Kubernetes Cluster Analysis

What engineering teams say about DrDroid

  • Rahul Bhattacharya
    "> "Earlier, debugging meant hopping between logs, workflows, and infra dashboards trying to piece together what went wrong. Dr. Droid pulls the context together and points us in the right direction — even someone new to the system can figure things out."
  • Moiz Arsiwala
    "> "One time I was woken up at 3am by a pager that escalated. I instantly asked DrDroid to investigate it and in a few minutes I was able to close the issue directly from Slack."
  • Prateek
    "> "What truly stood out was cost optimisation. While most platforms simply claim 20–30% savings, DrDroid actually helped us achieve meaningful cost reduction without even committing to a number upfront."