What if your data engineering pipeline could monitor itself, detect problems, investigate bottlenecks, optimize SQL queries, build data marts, and prepare dashboards with the help of AI agents?

Imagine starting your Monday morning and receiving this notification:

DATA ENGINEERING HEALTH REPORT

[!] Schema drift detected in customer_data.
[!] Data quality issue detected in daily_sales.
[!] AWS Glue job duration increased from 18 to 42 minutes.
[!] Redshift query identified as a performance bottleneck.

[+] Sales data mart prepared for validation.
[+] Executive dashboard refreshed.
[+] Pipeline investigation report generated.

Status: Human review required for proposed changes.

Instead of manually checking logs, inspecting SQL queries, investigating failed ETL jobs, and coordinating with analytics teams, you have a collection of specialized AI agents working together.

One agent monitors data quality.

Another investigates pipeline performance.

Another optimizes SQL.

Another builds analytical data models.

Another prepares dashboard specifications.

This is the vision behind Agentic Data Engineering.

Let's explore how to build this architecture using AWS.

1. Why Do We Need Agentic Data Engineering?

Traditional data engineering platforms are designed to ingest, transform, store, and deliver data.

However, as data platforms grow, operational complexity increases.

Common challenges include:

• Schema changes breaking existing ETL jobs.
• Missing and duplicate records affecting analytics.
• Data drift going unnoticed.
• AWS Glue jobs becoming slower.
• Expensive SQL queries affecting dashboard performance.
• Data marts requiring repeated manual development.
• Dashboards displaying stale or incomplete information.

These problems are interconnected.

A schema change can break a transformation. A failed transformation can delay a data mart. An outdated data mart can produce misleading business dashboards.

We need more than another ETL workflow.

We need an intelligent system that understands the complete data lifecycle.

2. The Multi-Agent Architecture

The proposed solution uses a central Orchestrator Agent and multiple specialized agents.

Each agent has a specific responsibility.

The Orchestrator coordinates these agents, collects their findings, and manages the workflow.

3. AWS Architecture

Here is the proposed architecture:

                 DATA SOURCES
                      |
          +-----------+-----------+
          |           |           |
       Databases     APIs      Streams
          |           |           |
          +-----------+-----------+
                      |
               Amazon EventBridge
                      |
                  Amazon SQS
                      |
                  AWS Lambda
                      |
                  Amazon S3
                   RAW ZONE
                      |
                   AWS Glue
                      |
              DATA QUALITY CHECKS
                      |
                  Amazon S3
                 CURATED ZONE
                      |
             AWS Glue Data Catalog
                      |
               AWS Lake Formation
                      |
          +-----------+-----------+
          |                       |
      Amazon Athena          Amazon Redshift
          |                       |
          +-----------+-----------+
                      |
                  DATA MARTS
                      |
                  Amazon Quick
                      |
                  DASHBOARDS


                 AGENTIC LAYER
                      |
               ORCHESTRATOR AGENT
                      |
       +--------------+--------------+
       |              |              |
    Drift Agent  Pipeline Agent   SQL Agent
       |              |              |
       +--------------+--------------+
                      |
              Data Modeling Agent
                      |
                Dashboard Agent
                      |
               Governance Agent
                      |
               APPROVAL WORKFLOW
                      |
              AWS Step Functions

AWS Services

4. Agent 1: Data Drift and Quality Detection

Let's consider a retail company receiving daily sales data.

Yesterday, the source schema was:

order_id
customer_id
product_id
quantity
unit_price
order_date

Today, a new field appears:

order_id
customer_id
product_id
quantity
unit_price
order_date
discount_code

The Drift Detection Agent identifies the schema change.

It investigates:

1. Is the new column expected?
2. Is it nullable?
3. Does it affect existing transformations?
4. Are downstream data marts dependent on the previous schema?
5. Does the change require approval?

The Data Quality Agent also checks the incoming records.

Example AWS Glue Data Quality rules:

rules = """
Rules = [
    IsComplete "order_id",
    IsComplete "customer_id",
    IsComplete "product_id",
    IsUnique "order_id",
    ColumnValues "quantity" > 0,
    ColumnValues "unit_price" >= 0
]
"""

Example findings:

Dataset: daily_sales

Duplicate order IDs: 245
Missing customer IDs: 12%
Invalid quantities: 38
Revenue anomaly: Detected

Recommendations:
- Quarantine invalid records.
- Investigate the source application.
- Compare with historical distributions.
- Validate downstream revenue calculations.

AWS Glue Data Quality performs the validation, while the AI agent interprets findings and recommends next steps.

5. Agent 2: Pipeline Performance Optimization

Imagine your AWS Glue job normally completes in 18 minutes.

Today, it takes 42 minutes.

The Pipeline Agent investigates:

• Job execution duration.
• Input and output volume.
• Worker utilization.
• Failed records.
• Retry counts.
• Data skew.
• Expensive joins.
• Partition pruning.
• Unnecessary transformations.

Example investigation report:

Pipeline: sales_curated_job

Historical duration: 18 minutes
Current duration: 42 minutes

Potential bottleneck:
Large shuffle during customer dimension join.

Recommendations:
1. Inspect join keys.
2. Review partition pruning.
3. Evaluate broadcast join eligibility.
4. Review worker configuration.
5. Benchmark the revised transformation.

The agent should compare similar workloads before concluding that a performance regression occurred.

6. Agent 3: SQL Query Optimization

Now imagine your analytics team reports that a monthly revenue query takes 90 seconds.

The SQL Optimization Agent inspects the query and execution plan.

Example:

SELECT
    store_id,
    DATE_TRUNC('month', order_date) AS sales_month,
    SUM(quantity * unit_price) AS revenue
FROM sales
WHERE order_date >= '2026-01-01'
GROUP BY
    store_id,
    DATE_TRUNC('month', order_date);

The agent can:

• Inspect query history.
• Retrieve execution plans.
• Identify expensive joins.
• Detect unnecessary scans.
• Review filtering conditions.
• Generate candidate SQL rewrites.
• Benchmark query performance.

For Amazon Redshift, EXPLAIN helps inspect the query execution plan.

The agent can also evaluate materialized views for frequently repeated analytical queries.

Important: A generated SQL query should never replace production SQL without validation and performance testing.

7. Agent 4: Automated Data Mart Builder

Once the data passes validation, the Data Modeling Agent prepares analytical models.

For retail analytics, it could recommend a star schema.

Fact table: fact_sales

order_id
customer_key
product_key
store_key
date_key
quantity
gross_revenue
discount_amount
net_revenue

Dimension tables:

dim_customer
dim_product
dim_store
dim_date

Example data mart SQL:

CREATE TABLE mart_monthly_sales AS
SELECT
    store_id,
    DATE_TRUNC('month', order_date) AS sales_month,
    SUM(quantity) AS units_sold,
    SUM(quantity * unit_price) AS gross_revenue,
    SUM(discount_amount) AS total_discount
FROM curated_sales
GROUP BY
    store_id,
    DATE_TRUNC('month', order_date);

The agent can propose table structures, generate SQL, and validate relationships.

Business definitions must be agreed upon before publishing the data mart.

For example, gross revenue and net revenue must not be treated as interchangeable metrics.

8. Agent 5: Dashboard Generation

Now comes the business intelligence layer.

The Dashboard Agent receives validated data mart metadata.

It identifies useful KPIs:

Amazon Quick can provide the visualization layer.

The agent can prepare dashboard specifications, KPI definitions, and refresh recommendations.

A dashboard should only be marked ready when its underlying data meets the required quality and freshness conditions.

9. Build the Agent Layer Using Strands

A practical approach is to explore Amazon Bedrock AgentCore with the Strands Agents SDK.

Strands provides a framework for building agents that can reason and use tools.

AgentCore provides infrastructure for deploying and operating agents.

A simplified Data Quality Agent:

from strands import Agent

quality_agent = Agent(
    system_prompt="""
    You are a Data Quality Investigation Agent.

    Responsibilities:
    - Analyze data quality results.
    - Identify missing and duplicate records.
    - Investigate schema changes.
    - Explain data anomalies.
    - Recommend safe remediation steps.

    Rules:
    - Never modify production data directly.
    - Never bypass validation rules.
    - Never expose sensitive customer data.
    - Request approval for high-impact changes.
    """
)

This is a starting example, not a complete production deployment.

Production implementation also requires authenticated tools, structured outputs, permissions, retries, error handling, and observability.

Example Agent Tool Registry

Orchestrator Agent
       |
       +-- inspect_source_schema()
       |
       +-- evaluate_data_quality()
       |
       +-- inspect_glue_job_metrics()
       |
       +-- analyze_query_plan()
       |
       +-- generate_sql_candidate()
       |
       +-- validate_data_mart()
       |
       +-- prepare_dashboard_spec()
       |
       +-- request_human_approval()

Each tool should have a narrowly scoped responsibility.

For example, the SQL Agent can execute read-only queries in a controlled environment, while production DDL requires a separate approval workflow.

10. Orchestrate Workflows with AWS Step Functions

AI agents should not independently control every production operation.

AWS Step Functions can manage deterministic workflows, retries, failure handling, and approval steps.

Example:

START
  |
  v
Discover Data
  |
  v
Check Schema
  |
  v
Run Data Quality
  |
  +---- Quality Failed ----> Investigate
  |                            |
  |                            v
  |                       Human Review
  |
  v
Run ETL
  |
  v
Monitor Performance
  |
  v
Analyze SQL
  |
  v
Build Data Mart
  |
  v
Validate Metrics
  |
  v
Publish Dashboard
  |
  v
END

The agents provide reasoning and recommendations.

Step Functions controls the workflow execution.

This separation makes the system easier to test, monitor, and govern.

11. Real-World Project: DataPilot AI

Let's turn this architecture into a practical project.

Project: DataPilot AI — Multi-Agent Data Engineering Control Center

The goal is to build one platform for monitoring and improving the entire data engineering lifecycle.

Core modules

Pipeline Health Center

• Pipeline execution status.
• Job duration.
• Failures and retries.
• Bottleneck investigation.

Data Quality Intelligence

• Missing values.
• Duplicate records.
• Schema changes.
• Anomaly reports.

SQL Performance Advisor

• Slow query identification.
• Query plan analysis.
• SQL optimization suggestions.
• Performance comparisons.

Data Mart Builder

• Source table discovery.
• Fact and dimension recommendations.
• SQL generation.
• Business metric validation.

Dashboard Generator

• KPI recommendations.
• Dataset preparation.
• Dashboard specifications.
• Refresh monitoring.

Agent Activity Center

• Agent execution status.
• Investigation reports.
• Recommendations.
• Approval history.
• Audit logs.

Suggested Technology Stack

Implementation Roadmap

1. Build the S3 data lake and Glue ingestion pipeline.
2. Add schema detection and data quality checks.
3. Deploy the Drift and Quality Agents.
4. Implement the Pipeline Performance Agent.
5. Add the SQL Optimization Agent.
6. Build the Data Mart Agent.
7. Integrate the Dashboard Agent.
8. Add approval workflows and governance.
9. Run end-to-end tests and production rollout.

Start with one dataset and two agents. Expand when the initial implementation demonstrates measurable value.

12. Security and Human Approval

An autonomous data engineering platform can make powerful changes.

That also creates risks.

An agent could generate incorrect SQL, misunderstand a business metric, or recommend a schema change that breaks downstream applications.

My recommended approach:

• Read-only access by default.
• Least-privilege IAM roles.
• Encryption using AWS KMS.
• Data access control through Lake Formation.
• Human approval for production changes.
• Testing generated code in isolated environments.
• Audit logs for agent decisions and tool calls.
• Monitoring through CloudWatch and agent observability tools.

Never allow an agent to bypass data governance or grant itself additional permissions.

The objective is controlled intelligence, not uncontrolled autonomy.

13. How Do We Measure Success?

The platform should be measured by business and engineering outcomes, not by the number of agents.

Track:

• Mean Time to Detect data issues.
• Mean Time to Resolve pipeline failures.
• Data quality pass rate.
• ETL execution duration.
• SQL query execution time.
• Data mart freshness.
• Dashboard refresh success rate.
• Recommendation acceptance rate.
• Cost per successful pipeline run.

Establish a baseline before introducing automation and compare the results afterward.

Not every anomaly is a real problem, and not every AI recommendation improves performance. Continuous validation matters.

14. The Future of Data Engineering

Imagine asking your Data Engineering Control Center:

"Why did yesterday's sales dashboard show a 20% revenue decline?"

The Orchestrator coordinates the agents:

1. Quality Agent checks missing and duplicate records.
2. Drift Agent compares current and historical schemas.
3. Pipeline Agent investigates delayed or failed jobs.
4. SQL Agent checks analytical query changes.
5. Data Mart Agent validates revenue calculations.
6. Dashboard Agent checks dataset freshness.

You receive a consolidated explanation supported by evidence.

You review the proposed solution and approve the necessary changes.

That's the real opportunity behind agentic data engineering.

It isn't about replacing data engineers.

It's about giving them an intelligent team that can help manage increasingly complex data platforms.

Final Thoughts

The next generation of data engineering will not be defined only by how quickly we move data.

It will also be defined by how intelligently we monitor, validate, optimize, govern, and deliver that data.

With AWS Glue, Amazon S3, Amazon Redshift, AWS Step Functions, Amazon Bedrock AgentCore, Strands Agents SDK, and Amazon Quick, we can start building these intelligent workflows.

From raw data to trusted insights — with AI agents working together.

Let's Discuss

If you could build only one specialized AI agent for your data engineering environment, which would you choose?

1. Data Quality and Drift Detection Agent
2. Pipeline Performance Optimization Agent
3. SQL Query Optimization Agent
4. Automated Data Mart Builder
5. Dashboard Generation Agent

Share your thoughts in the comments!

References

• Amazon Bedrock AgentCore Documentation
• Strands Agents SDK
• AWS Glue Data Quality
• AWS Glue Data Quality Anomaly Detection
• AWS Step Functions
• Amazon Redshift Query Plan Analysis
• Amazon Redshift Automated Materialized Views
• Amazon Quick Documentation

AWS #AgenticAI #DataEngineering #AmazonBedrock #AWSGlue #GenerativeAI