L1 Field Note
Databases The Database Observability Baseline: What Every DBA Dashboard Must Show
Before you can adopt AI-assisted triage, your database dashboard needs a foundation built on saturation, locking, and lag metrics.
Series / AI Engineering
Observability 2024–2026
Three years of production observability: from building the first database dashboard, through AI-assisted incident triage, to autonomous SRE agents that observe themselves.
15 posts AI Engineering
Who This Is For
Platform engineers, DBAs, and SREs who own observability end-to-end — from setting up the first Prometheus exporter to deciding whether to let an agent execute remediation without human approval.
What You Will Be Able to Do
- Build a database observability baseline that surfaces saturation before users notice
- Instrument AI-assisted triage so alert noise collapses into a single root-cause hypothesis
- Govern telemetry cost so the observability bill doesn't exceed the infrastructure bill
- Monitor AI agents, MCP servers, and autonomous SRE pipelines using purpose-built telemetry
Prerequisites
Production experience with at least one database and one alerting system. No AI background required — the 2026 posts treat agents as distributed systems, not magic.
1 Foundation — 2024
What every production observability stack must measure: database dashboards, per-engine instrumentation, open-source tooling, and cost visibility.
L2 Deep Dive
Databases CloudWatch Database Insights for Aurora and RDS: The New AWS Monitoring Center
How to use CloudWatch and Performance Insights to root-cause Aurora and RDS incidents without deploying third-party agents.
L2 Deep Dive
Databases PostgreSQL Observability: Vacuum, Bloat, Locks, Replication Lag, and Query Plans
Monitoring PostgreSQL requires looking past the operating system and into the internal bookkeeping of MVCC, autovacuum, and replication streams.
L2 Deep Dive
Databases Cassandra Observability: Compaction, Tombstones, Repair, Latency, and Hot Partitions
Why generic server monitoring fails for Apache Cassandra, and how to track the true operational signals of a distributed masterless database.
L1 Field Note
Databases Prometheus + Grafana for Database Engineers: Open-Source Monitoring That Actually Works
How to position Prometheus and Grafana as the open-source baseline for teams that cannot send every byte of database telemetry to managed services.
L2 Deep Dive
Engineering Fundamentals Cost Observability: Build Dashboards That Show Waste Before Finance Finds It
How to expand monitoring beyond uptime by building dashboards that expose underutilized RDS instances, EBS io2 waste, and backup retention drift.
2 AI-Assisted Operations — 2025 H1
When generative AI enters the on-call loop: compressing 500 alerts into a root-cause hypothesis, commercial AI SRE tooling, and the end of single-signal alerting.
L2 Deep Dive
AI Engineering AI-Assisted Incident Triage: From Alert Noise to Root-Cause Hypotheses
How generative AI tools like CloudWatch Investigations shift the operational burden from reading raw dashboards to validating machine-generated hypotheses.
L2 Deep Dive
AI Engineering Datadog Bits AI SRE: What an AI On-Call Teammate Changes for DBAs
How autonomous AI agents like Bits AI SRE are shifting the database incident workflow from manual dashboard hunting to conversational investigation.
L2 Deep Dive
System Design The End of Single-Signal Alerting: Correlating Metrics, Logs, Traces, Deployments, and Cost
Why paging an engineer solely because CPU hit 85% is an anti-pattern, and how to build correlated alerts that require real operational evidence.
3 Cost Governance & Alert Discipline — 2025 H2
Scaling observability without breaking the budget: tying cloud cost to workload and team, eliminating alert fatigue, and governing telemetry spend at the pipeline level.
L2 Deep Dive
AI Engineering FinOps Observability: Tie Cloud Cost to Workload, Team, Product, and Customer
How to connect engineering telemetry with cost telemetry to achieve granular cloud unit economics using FinOps principles and FOCUS standards.
L1 Field Note
Engineering Fundamentals Alert Fatigue Engineering: How to Build Fewer, Better, Actionable Alerts
A dashboard is not observability, and an alert without a specific action is just operational debt masquerading as monitoring.
L2 Deep Dive
AI Engineering Telemetry Cost Control: Why Observability Data Itself Needs Governance
If you log everything and monitor every dimension, your observability bill will eventually exceed your database infrastructure bill. Here is how to fix it.
4 Agentic SRE — 2026
Autonomous operations: monitoring the agents themselves, observing MCP control planes, and the reference architecture for safe human-in-the-loop remediation.
L2 Deep Dive
AI Engineering AI Agent Observability: Monitor Tool Calls, Token Spend, Latency, and Failure Loops
Why monitoring autonomous SRE agents requires tracking tool-call hallucinations, context window saturation, and recursive retry loops, rather than just basic CPU metrics.
L2 Deep Dive
AI Engineering MCP Server Observability: The New Control Plane for AI + Enterprise Tools
How the Model Context Protocol (MCP) became the networking layer for AI agents, and why monitoring these connections is critical for enterprise security.
L2 Deep Dive
AI Engineering Agentic SRE Architecture: Skills, Agents, MCP Servers, and Human Approval Loops
The definitive 2026 reference architecture for autonomous database operations, from detection to multi-agent diagnosis to human-in-the-loop remediation.