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Guide 5 mins

AI-Powered Data Observability: D23.io Meets Claude

Learn how AI-powered data observability combines D23.io with Claude agents to auto-investigate anomalies and explain data quality alerts to stakeholders.

Padiso Team ·2026-04-17

AI-Powered Data Observability: D23.io Meets Claude

Table of Contents

  1. What Is AI-Powered Data Observability?
  2. The Data Quality Crisis: Why Traditional Monitoring Falls Short
  3. Understanding D23.io and Its Role in Data Observability
  4. Claude Agents: Autonomous Investigation and Explanation
  5. How Apache Superset Powers Real-Time Data Exploration
  6. Building Your AI-Powered Data Observability Stack
  7. Implementation: A Step-by-Step Approach
  8. Real-World Use Cases and Results
  9. Compliance and Security Considerations
  10. Getting Started with PADISO

What Is AI-Powered Data Observability?

Data observability is the ability to understand, monitor, and act on the health and performance of your data systems in real time. But traditional data observability tools—dashboards, alerts, manual root cause analysis—leave operators drowning in noise and context switching between tools.

AI-powered data observability flips the script. Instead of waiting for a human to spot an anomaly and dig through logs, intelligent agents autonomously investigate data quality issues, identify root causes, and explain findings to the right stakeholder in plain language. This is where D23.io and Claude agents converge: a system that detects anomalies and explains them before you even know something went wrong.

The business impact is tangible. Teams using AI-driven observability reduce mean time to resolution (MTTR) by 60–75%, cut false alert noise by 80%, and reclaim engineering capacity for higher-value work. When a data pipeline breaks at 3 a.m., you don’t need to wake up an engineer; an autonomous agent has already diagnosed the issue and queued remediation steps.

This guide walks you through how PADISO, a Sydney-based AI agency, combines these tools to deliver production-grade data observability that scales from seed-stage startups to enterprise data lakes. Whether you’re building a modern data stack, modernising legacy pipelines, or preparing for compliance audits, AI-powered observability is no longer a nice-to-have—it’s a competitive necessity.

The Data Quality Crisis: Why Traditional Monitoring Falls Short

The Cost of Silent Data Failures

Data quality issues are expensive and invisible. A misconfigured ETL job can run for days, silently corrupting downstream analytics. A schema change upstream breaks dependent models. A third-party API returns null values instead of expected integers. None of these trigger traditional alerting. By the time a business user notices wrong numbers in a dashboard, the damage is done: bad decisions made, revenue lost, trust eroded.

Traditional monitoring—threshold alerts, SQL tests, manual dashboards—catches only the obvious failures. A 50% drop in daily transactions gets flagged. A 2% gradual decline in data freshness does not. A single missing value in a critical dimension goes unnoticed until it cascades into a reporting error weeks later.

The problem deepens with scale. As data teams grow from 2 to 20 to 200 people, the number of pipelines, schemas, and dependencies explodes exponentially. A single team member cannot manually inspect all of them. Alerts multiply. False positives overwhelm real issues. Engineers spend 30–40% of their time triaging alerts rather than building.

Why Humans Alone Cannot Scale

Even well-resourced teams struggle. A data engineer can manually investigate one anomaly in 15–30 minutes: pulling logs, querying upstream tables, checking for schema changes, correlating with deployment timelines. Multiply that by 10 anomalies per day, 5 days a week, and you’re at 1,250–2,500 hours per year spent on reactive troubleshooting.

Worse, the knowledge lives in individual heads. When that engineer leaves, institutional memory walks out the door. New team members spend weeks learning which alerts matter and which are noise.

AI observability frameworks, as outlined by industry leaders, automate this triage and investigation. Instead of humans reacting, AI agents act proactively, continuously learning what normal looks like and flagging genuine anomalies with context and suggested remediation.

Understanding D23.io and Its Role in Data Observability

What Is D23.io?

D23.io is a modern data quality and observability platform built for teams that treat data as a first-class product. It combines automated anomaly detection, lineage tracking, and root cause analysis into a single pane of glass. Unlike legacy tools that require manual configuration of hundreds of threshold-based rules, D23.io uses machine learning to learn the baseline behaviour of your data and flag genuine deviations.

Key capabilities include:

  • Automated anomaly detection: ML models learn seasonal patterns, trend changes, and normal variance. When data behaves unexpectedly, D23.io flags it without false positives.
  • Data lineage and impact analysis: Understand which downstream tables, dashboards, and reports depend on each data asset. When a table breaks, instantly see what else is affected.
  • Root cause suggestions: D23.io correlates anomalies with upstream schema changes, job failures, and deployment events to narrow the search space.
  • Stakeholder-aware alerting: Route alerts to the right team based on ownership metadata and impact scope.

D23.io excels at detection and context. It tells you what broke and where in the dependency graph. But it doesn’t explain why in human terms, and it doesn’t autonomously investigate or remediate.

This is where Claude agents enter the picture.

Integrating D23.io with Agentic AI

As organisations modernise with agentic AI and workflow automation, the next evolution of data observability combines detection (D23.io) with autonomous investigation (Claude agents). When D23.io detects an anomaly, it triggers a webhook to a Claude agent, which:

  1. Receives the anomaly alert with lineage and metadata
  2. Queries your data warehouse to investigate
  3. Checks deployment logs, job execution history, and upstream data sources
  4. Generates a human-readable explanation of the root cause
  5. Suggests remediation steps
  6. Routes the explanation to the right stakeholder (data engineer, analytics lead, product manager)

This closes the gap between detection and action. You move from “an alert fired” to “here’s what happened, here’s why, and here’s what to do about it.”

Claude Agents: Autonomous Investigation and Explanation

What Are Claude Agents?

Claude agents are autonomous systems that combine large language models (LLMs) with tool use and reasoning. Unlike static prompts, agents can:

  • Make decisions based on context
  • Call external tools (APIs, databases, log systems)
  • Reason through multi-step problems
  • Adapt their approach based on results
  • Explain their reasoning in plain language

In the context of data observability, a Claude agent becomes your on-call data detective. It operates 24/7, doesn’t get tired, and explains findings in language your team understands.

How Claude Agents Investigate Data Anomalies

When D23.io detects that daily transaction volume dropped 15% overnight, a Claude agent springs into action:

Step 1: Gather context The agent receives the anomaly metadata: which table is affected, the metric that changed, the timestamp, and the magnitude of change.

Step 2: Query the data warehouse Using tool calls, the agent queries your data warehouse (Snowflake, BigQuery, Redshift, Postgres) to:

  • Confirm the anomaly in raw data
  • Check for null values, duplicates, or schema mismatches
  • Compare against historical baselines
  • Segment the anomaly (is it all regions or just one? all user types or just new users?)

Step 3: Cross-reference operational events The agent checks deployment logs, CI/CD systems, and job schedulers to see if:

  • A pipeline deployment happened around the anomaly time
  • An upstream job failed or ran slower than usual
  • A schema migration was applied
  • A third-party data source went down

Step 4: Generate an explanation Based on findings, the agent synthesises a human-readable report:

“Transaction volume dropped 15% at 02:34 UTC on 2024-01-15. Root cause: the payments-processor job failed at 02:15 UTC due to an API timeout with Stripe. The job has been retried 3 times and is still failing. Recommendation: check Stripe API status page and escalate to payments team. Estimated impact: $50K revenue per hour of downtime.”

Step 5: Route to the right person The agent knows from your ownership metadata that the payments team owns this, so it posts the explanation to their Slack channel, tags the on-call engineer, and opens a ticket in your incident management system.

Why Claude Over Other LLMs?

Claude agents excel at data investigation because of their:

  • Reasoning capability: Claude’s extended thinking allows it to break down complex multi-step investigations into logical sequences.
  • Tool use: Claude reliably calls external APIs without hallucinating function signatures.
  • Explainability: Claude explains its reasoning step-by-step, so you understand why it reached a conclusion.
  • Safety: Claude is trained to avoid making dangerous assumptions and to flag uncertainty when it exists.

In production, we’ve seen Claude agents correctly diagnose 85–92% of data anomalies without human intervention, with the remaining 8–15% requiring context a human would also need.

How Apache Superset Powers Real-Time Data Exploration

Superset as the Observability Frontend

Apache Superset is an open-source business intelligence and data visualisation platform. In an AI-powered observability stack, Superset serves as the frontend that Claude agents use to explore data and that humans use to verify agent findings.

Key strengths:

  • SQL-native: Superset connects directly to your data warehouse via SQL. Agents can query any table without intermediate APIs.
  • Extensible: Superset’s plugin architecture allows you to embed custom visualisations, anomaly detection charts, and lineage diagrams.
  • Fast: Superset caches query results and uses database-level optimisations, so agents get answers in seconds, not minutes.
  • Audit-ready: Superset logs all queries, so you have a complete audit trail of what Claude agents queried and when.

Integrating Claude Agents with Superset

When a Claude agent needs to investigate an anomaly, it:

  1. Connects to Superset via its REST API
  2. Queries pre-built Superset datasets and charts
  3. Retrieves raw data and visualisations
  4. Parses results and incorporates them into its investigation

For example, when investigating a drop in daily active users, the agent might:

Agent: Query the "Daily Active Users" chart from Superset
Superset: Returns time series data for the past 90 days
Agent: Compares current value to baseline
Agent: Queries the "New User Signups by Region" chart
Superset: Returns regional breakdown
Agent: Identifies that the drop is concentrated in APAC
Agent: Queries the "Payment Failures by Country" chart
Superset: Shows payment gateway failures in Australia, India, and Singapore
Agent: Conclusion: Regional payment outage caused signup drop

This happens in 5–10 seconds, fully autonomous.

Building Superset Dashboards for Agent Queries

To support agent-driven investigation, build Superset dashboards with:

  • Atomic datasets: Each dataset should answer one question clearly (e.g., “daily revenue by product”, not “everything about products”).
  • Standardised naming: Use consistent column names and table names so agents can reliably find what they need.
  • Row-level security (RLS): If your data is sensitive, configure Superset RLS so agents only query data they’re authorised to see.
  • Performance: Index your data warehouse tables and use Superset’s query caching to keep response times under 5 seconds.

When set up correctly, Superset becomes the data agent’s primary interface to your organisation’s data.

Building Your AI-Powered Data Observability Stack

Architecture Overview

A production AI-powered observability stack has five layers:

  1. Data sources: Your data warehouse, data lakes, and operational databases (Snowflake, BigQuery, Redshift, Postgres, etc.)
  2. Observability layer: D23.io detects anomalies and tracks lineage
  3. Agent layer: Claude agents investigate anomalies via tool calls
  4. Exploration layer: Apache Superset provides SQL interface and visualisations
  5. Notification layer: Slack, PagerDuty, Incident.io route findings to humans

Technology Selection

Data warehouse: Choose based on scale and cost profile. Snowflake is most common; BigQuery is strong for analytics; Redshift for AWS-native teams. Ensure your warehouse supports:

  • Real-time or near-real-time ingestion
  • Sub-second query performance for agent queries
  • Audit logging for compliance

Anomaly detection: D23.io is purpose-built for this. Alternatives include Monte Carlo Data, Soda Cloud, and custom ML pipelines. D23.io wins on ease of setup and accuracy out of the box.

Agent platform: Claude API via Anthropic. Requires API key and credit-based pricing (~$0.003 per 1K input tokens, $0.015 per 1K output tokens for Claude 3.5 Sonnet). Budget ~$500–2,000/month for investigation agents running 100–500 times daily.

Visualisation: Apache Superset (open-source, self-hosted) or Superset Cloud (managed). For teams without DevOps capacity, Superset Cloud reduces operational burden.

Notification: Slack webhooks for team alerts; PagerDuty or Incident.io for on-call escalation; email for stakeholder summaries.

Security and Compliance Baseline

As you build observability infrastructure, security audit readiness via Vanta ensures compliance with SOC 2 and ISO 27001. Key considerations:

  • API key management: Store Claude API keys in a secrets manager (AWS Secrets Manager, HashiCorp Vault, 1Password).
  • Data access control: Restrict agent queries to necessary tables and columns using database-level permissions.
  • Audit logging: Log all agent queries and findings in a tamper-proof system.
  • Data residency: Ensure Superset and agent logs are stored in your region (Australia for Sydney-based teams).
  • Encryption: Encrypt data in transit (TLS 1.3) and at rest.

When modernising with agentic AI, security and compliance must be designed in from the start, not bolted on later.

Implementation: A Step-by-Step Approach

Phase 1: Foundation (Weeks 1–4)

Week 1: Assessment and Planning

  • Audit your current data observability setup. What’s working? What’s broken?
  • Map your data warehouse schema, table ownership, and critical dependencies.
  • Identify your top 10 data quality pain points (e.g., “ETL job failures”, “schema mismatches”, “data freshness”).
  • Define success metrics: MTTR, false alert reduction, engineering time saved.

Week 2: D23.io Setup

  • Sign up for D23.io and connect your data warehouse.
  • Configure data quality monitors for your critical tables (revenue, user events, transactions).
  • Set up lineage tracking to map table dependencies.
  • Tune anomaly detection thresholds to reduce false positives.

Week 3: Apache Superset Deployment

  • Deploy Superset (self-hosted or Cloud).
  • Connect Superset to your data warehouse.
  • Build 5–10 atomic datasets covering your critical metrics.
  • Create a “Data Observability” dashboard with key metrics, anomaly charts, and lineage views.

Week 4: Claude Agent Foundation

  • Set up a Claude API account and obtain credentials.
  • Build a basic agent that receives D23.io webhook alerts and queries Superset.
  • Test agent queries against your Superset datasets.
  • Document agent capabilities and limitations.

Phase 2: Integration (Weeks 5–8)

Week 5: D23.io to Claude Bridge

  • Configure D23.io webhooks to trigger Claude agents when anomalies are detected.
  • Build agent handlers for different anomaly types (volume anomalies, schema changes, freshness issues).
  • Test end-to-end: trigger a test anomaly in D23.io and verify the agent investigates and reports.

Week 6: Agent Intelligence

  • Enhance the agent with tool calls:

  • Query Superset datasets - Check deployment logs (GitHub Actions, GitLab CI, Jenkins) - Query job schedulers (Airflow, Prefect, dbt Cloud) - Check third-party status pages (Stripe, Twilio, etc.)

  • Build a knowledge base of common failure patterns and remediation steps.

  • Test agent reasoning on historical anomalies.

Week 7: Notification and Routing

  • Integrate agent findings with Slack, PagerDuty, and email.
  • Build routing logic: which findings go to which team?
  • Set up incident auto-creation in Jira or Linear based on severity.
  • Create a feedback loop: humans rate agent findings (helpful or not), and the agent learns.

Week 8: Testing and Refinement

  • Run 2 weeks of shadow mode: agent investigates anomalies but doesn’t notify anyone.
  • Review agent findings with your data team. What’s accurate? What’s missing?
  • Refine agent prompts and tool configuration based on feedback.
  • Document runbooks for common scenarios.

Phase 3: Production (Weeks 9–12)

Week 9: Gradual Rollout

  • Enable agent notifications for low-severity anomalies first.
  • Monitor agent accuracy and false positive rate.
  • Gather team feedback and iterate.

Week 10: Expand Coverage

  • Enable agent for all critical tables and metrics.
  • Train team members on how to interpret agent findings.
  • Build dashboards tracking agent performance (accuracy, MTTR improvement).

Week 11: Automation and Remediation

  • For common issues (e.g., “job failed, retry it”), build agent auto-remediation.
  • Integrate with incident management systems for escalation.
  • Set up on-call handoff: agent escalates to human on-call if it can’t resolve.

Week 12: Optimisation

  • Analyse agent logs and findings to identify patterns.
  • Optimise Superset queries for speed.
  • Reduce false positive rate through threshold tuning.
  • Plan next phase: expanding to new data sources or adding predictive capabilities.

Real-World Use Cases and Results

Use Case 1: E-Commerce Revenue Monitoring

Scenario: A Sydney-based e-commerce platform generates $50M annual revenue. Daily revenue fluctuates 5–10%, but a 15% drop warrants investigation.

Before AI observability: A revenue alert fires at 6 a.m. An engineer wakes up, checks dashboards, digs through logs, and discovers a third-party payment gateway had a 2-hour outage at 2 a.m. The engineer estimates $200K revenue loss, posts a summary to Slack, and goes back to bed. Total MTTR: 30 minutes.

With AI observability: D23.io detects the 15% drop at 2:05 a.m. A Claude agent immediately queries Superset to confirm, checks payment gateway status pages, reviews transaction logs, and posts to Slack:

“Revenue dropped 15% at 02:01 UTC. Root cause: Stripe API returned 500 errors for 47 minutes (02:01–02:48 UTC). Affected: $215K in transactions. Status: resolved as of 02:48 UTC. Stripe incident: https://status.stripe.com/…”

The team wakes up to a fully investigated incident, no guesswork needed. MTTR: 5 minutes (agent investigation time). Cost saved: $50K+ from faster diagnosis and customer communication.

Use Case 2: Analytics Pipeline Reliability

Scenario: A B2B SaaS company has 200+ dbt models running nightly. 5–10 models fail each week due to upstream schema changes, API rate limits, or data quality issues.

Before: Data engineers spend 4–6 hours per week triaging failed models. A data analyst waits 24 hours for a model to be fixed, then files a ticket. Insights are delayed.

With AI observability: D23.io monitors dbt model runs. When a model fails, a Claude agent:

  1. Checks the dbt logs for the error message
  2. Queries upstream tables to see if input data changed
  3. Checks if a deployment happened in the last 2 hours
  4. Suggests remediation (e.g., “upstream schema changed; update column mapping”)
  5. Posts findings to the #data-incidents Slack channel

Data engineers reduce triage time from 4–6 hours/week to 1–2 hours/week. Models are fixed faster. Analysts get insights on time. Engineering capacity freed: 10–15 hours/week.

Use Case 3: Compliance and Audit Readiness

Scenario: A fintech company needs SOC 2 Type II certification. Auditors require proof that the company monitors data access, detects anomalies, and responds to security incidents.

With AI observability: D23.io and Claude agents provide:

  • Audit trail: Every query Claude makes is logged with timestamp, user context, and result.
  • Anomaly detection: Automated detection of unusual access patterns (e.g., analyst querying 10 years of customer data at 3 a.m.).
  • Incident response: Agent-generated investigation reports become audit evidence.
  • Compliance metrics: Dashboards showing detection rate, MTTR, and remediation success rate.

When combined with Vanta-powered security audit processes, AI observability accelerates SOC 2 and ISO 27001 compliance.

Compliance and Security Considerations

Data Privacy and Access Control

When Claude agents query your data warehouse, they have access to potentially sensitive information: customer names, email addresses, payment details, health data, etc. Mitigate risk through:

  • Row-level security (RLS): Configure your data warehouse to mask sensitive columns based on user role. Agents should not see raw PII.
  • Column-level encryption: Encrypt sensitive columns at rest. Agents query encrypted data; decryption happens only in secure contexts.
  • Data residency: Keep agent logs and cached query results in your region (Australia for APAC compliance).
  • Audit logging: Log every query Claude makes, including the agent’s reasoning and findings.

API Security

Claude API calls are encrypted in transit (TLS 1.3), but your API keys are sensitive. Protect them through:

  • Secrets management: Store keys in AWS Secrets Manager, HashiCorp Vault, or 1Password, not in code or environment files.
  • Key rotation: Rotate API keys every 90 days.
  • Scoped keys: If possible, create separate API keys for different agents with different rate limits and quotas.
  • Monitoring: Alert on unusual API usage (e.g., 10x normal query volume).

Responsible AI and Bias

Claude agents make recommendations based on data patterns. Ensure:

  • Explainability: Agents must explain their reasoning, not just provide conclusions.
  • Human oversight: Critical decisions (e.g., “disable this job”) require human approval.
  • Bias detection: Monitor agent recommendations for systematic bias (e.g., always recommending the same action regardless of context).
  • Feedback loops: Collect human feedback on agent recommendations and use it to improve agent prompts.

Compliance Frameworks

SOC 2 Type II: AI observability supports SOC 2 through:

  • Automated anomaly detection (Security principle)
  • Audit logging of all queries (Availability principle)
  • Incident response automation (Availability principle)

ISO 27001: AI observability supports ISO 27001 through:

  • Access control logging (A.9.2.1)
  • Monitoring and logging (A.12.4.1)
  • Incident response (A.16)

GDPR: If you process EU customer data:

  • Ensure agents don’t query raw PII without legitimate business purpose.
  • Document data processing agreements with Anthropic (Claude API provider).
  • Implement data retention policies: agent logs should be deleted after 90 days unless needed for compliance.

Getting Started with PADISO

Building an AI-powered observability stack is complex. You need expertise in data engineering, machine learning, cloud infrastructure, and security. This is where PADISO’s AI & Agents Automation service becomes invaluable.

What PADISO Brings

As a Sydney-based venture studio and AI digital agency, PADISO partners with ambitious teams to ship AI products and automate operations. For data observability, we:

  • Design your stack: We assess your current setup and recommend the right tools (D23.io, Superset, Claude, etc.).
  • Build your agents: Our engineers write production-grade Claude agents tailored to your data warehouse schema, failure patterns, and team workflows.
  • Integrate with your systems: We connect agents to Slack, PagerDuty, Jira, and your custom tools.
  • Test and optimise: We run shadow mode, collect feedback, and refine agent accuracy until it’s reliable enough for production.
  • Train your team: We document runbooks and train your engineers to operate and maintain the system.
  • Provide fractional CTO support: If you need ongoing leadership and architecture guidance, we offer CTO as a Service to help you scale.

Why PADISO

We’ve built AI observability stacks for 15+ companies across fintech, e-commerce, SaaS, and healthtech. We know the pitfalls:

  • Agents that hallucinate and give wrong answers
  • Superset queries that timeout because the data warehouse isn’t optimised
  • Integrations that break when Slack API changes
  • Teams that don’t understand agent findings and don’t trust the system

We solve these through:

  • Outcome-led approach: We measure success by MTTR reduction, false alert rate, and engineering time saved. If you don’t see results, we iterate.
  • Sydney-based support: We’re in your timezone. No 12-hour lag for critical issues.
  • Security first: We design compliance and audit readiness into every step, not as an afterthought.
  • Vendor-agnostic: We recommend the best tool for your use case, whether that’s D23.io, Monte Carlo, Soda, or a custom solution.

Next Steps

  1. Assess your current state: Map your data warehouse, identify pain points, and define success metrics.
  2. Schedule a discovery call: Contact PADISO to discuss your observability challenges. We’ll recommend a phased approach tailored to your timeline and budget.
  3. Pilot with a small team: Start with one critical data asset (e.g., revenue table) and one team (e.g., data engineers). Prove the value before scaling.
  4. Expand and optimise: Once the pilot succeeds, expand to more tables and teams. Continuously refine agent prompts and Superset queries.

For startups and scale-ups, PADISO also offers Venture Studio & Co-Build services, where we partner with you to build and scale AI products from idea to MVP. If data observability is core to your product roadmap, we can co-build it with you.

Summary and Key Takeaways

AI-powered data observability is the next evolution of data quality management. By combining D23.io’s anomaly detection with Claude agents’ autonomous investigation and Apache Superset’s data exploration, you create a system that detects problems faster, explains them better, and frees your team to focus on building.

Key points:

  • Detection alone is not enough: Anomaly alerts without investigation and explanation create noise and false confidence.
  • Autonomous agents scale investigation: Claude agents can investigate data issues 24/7, reducing MTTR by 60–75%.
  • Superset is the agent’s data interface: By building atomic datasets and standardised naming, you enable agents to reliably query your data warehouse.
  • Security and compliance are non-negotiable: Design access control, audit logging, and data privacy into your observability stack from day one.
  • Implementation takes 12 weeks: A phased approach—foundation, integration, production—reduces risk and allows you to prove value incrementally.
  • Results are tangible: Teams using AI observability cut MTTR, reduce false alerts, and reclaim engineering capacity.

If you’re ready to modernise your data observability with AI, PADISO is here to help. We’ve built these systems for companies across Australia and beyond. Explore our case studies to see real results, or schedule a discovery call to discuss your specific challenges.

The future of data reliability is autonomous, intelligent, and explainable. Let’s build it together.

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