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Slides: Using RL Agent to Detect and Remediate ETL Pipeline Failures - Anna Marie Benzon

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Using RL Agent to Detect and Remediate ETL Pipeline Failures - Anna Marie Benzon

Relationship To World's Fair 2026

These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

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Slide text:

Using RL-based Agent to Detect and Remediate ETL Pipeline Failures

Presenter: Anna Marie Benzon

University of the Philippines, Diliman

github.com/ambenzon27/rl-etl-remediation-agent

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Slide text:

Failure Inspect Logs Diagnose Repair Rerun Validate

Late or unavailable source data

The 日日日 Schema drift

Problem Null-rate spikes Datetime parsing incompatibilities

Cloud ETL jobs break because of:

Type changes

Unknown runtime errors

Modeled manual MTTR ~ 2.5 working days

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Slide text:

Phate 1 Architecture -- Al Pipeline Heulth Agent

An End-to-End RL Pipeline

Health Agent for Anomaly

Detection and Autonomous

Self-Healing of Cloud Data

t tt+e+ + At Pipelines

t cttt th

: NOTE: GENERAUZED PUDUKC REFERENCE ARCHTTECTURE

Solution The Monitor → Diagnose → Score → Decide → Safety → Act Validate 2.Diagnose the likely failure family: 5.Validate whether the action restored a healthy state 3.Estimate operational risk 4.Select a bounded remediation action 1.observe logs, schema, and data-quality conditions

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Slide text:

Deterministic Anomaly Rules Schema drift, null spikes, field

removals, type changes

Q-Learning Decision Policy Retry, coerce schema, rollback,. quarantine, escalate, log The

Critical anomaly.+ passive action Safety Override Intelligence

- escalate Layer

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Slide text:

Detection and Diagnosis

Establish the Facts Before Choosing an Action

Component Responsibility

Schema profiler Structure, types, nesting. null rates

Drift detector Additions.removals.type changes

Data-quality analyzer Completeness, validity. consistency

Error classifier Converts log patterns into failure categories

Risk scorer Produces an operational risk level

Deterministic prototype with limited dataset; ML-ready with richer incident history.

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Slide text:

RL Selects the Response, Not the Facts

State: Action:.

retry [coerce] rollback quarantine oscalato [log]

1.Failure category

4.Drift severity 3.Retry count 2.Risk level 2.Small, interpretable statespace: 3.Low-memory.inference: 1. Tabular Q-learning

5.Data-quality condition 4.Inspectable Q-values for every decision

TeChNICALlY, ThIS IS a SInGLE-StEp CONTexTUAL DECISIoN prOBLem IMPlEmENTeD WiTH TaBULaR Q-LeaRnInG.:

-:-

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Slide text:

Safe Autonomy

The Learned Policy Does Not Have Final Authority

1. Q-learning policy proposes an action

2. Safety layer evaluates critical anomalies

3. Unsafe passive actions are overridden

4. High-risk and unknown conditions escalate

5. Every decision produces an audit record

Escalation is a correct action, not a failure of autonomy.

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Slide text:

Example Failure

ntriggered_at: "2026-05-21te2:05:20z, "job_name": " Synthotic_etl Job ", - [A] Real Gtue job failure

"error_classification": { "error_tyPe": "PAIeTIME_FOfRAL ERROR". "confidence": o.90, - (B] classified by Error Classifle

scheaa/date foreat y. "recoamended_action": "APPLY_SCHEHA_COERCIoN" - [C] Rule engine says: coerce

"anonaly_override": fatse, - [E] Safety override did not fire + [o] Agent selects scheas coerclon

"reaedlation": { "note": -Hanual Gtue script update required" - [F] Loggcd for review "success": falsc,

Observed condition 1.Glue-style job fallure event Decision? 1.Policy selects schema coercion

received 2.safety override does not fire

2:Datetime-format incompatibility detected 3.Automatic coercion.is unavailable 4.lncident is logged for manual reviev

3.Error classified with 0.90

confidence

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Slide text:

ReproducibleEvaluation

Designed for Independent Reproductior

1.Generalized AWs Lambda-style remediation-agent ambenzon27/rl-etl-

2.synthetic schemas, records, logs, and incidents architecture PLitLided ETL rerptataon L(ert ia tchtra -drn tvhtle brchmMi, ard Ais Llnbdh deyitor, enar cintilcenor, Qremrlng dtctTenr Yo

5.Robustness check across 30 runs, seeds 4.Four controlled experiments: El-E4 3.No production data or infrastructure identifiers: GTEo AL guita E?l Ttltdttan tgtl 1r TaTtai tttatr, tt atia Dulewto uoa.uwp oie 'uayphtp upio

6.Results reported with 95% confidence intervals 42-71 available in the GitHub Public benchmark and tests are

repository

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Slide text:

Evaluation Results

Controlled synthetic benchmark 30 seeds, mean ± 95% cl Htean iie to itcdiey, shotn on a log scale MTTR Drops from Days to Minutes in the Benchmark

1.Rule-based anomaly detector 10.000 2.5 working days

2.Recall: 0.800 Precision' 1.000 Loco -99.85% 10vor MTTR

3.FI: 0.889 100

4.Rl successful-cdse resolution tim approximately.5.2 minutes 5.24 +l 0.14 pnn

5.RL simulated success rate: 74.63 ± 1.51%

tion rate: 88.63 ± 0.89% nadent rtspord+ KSEE syher bechat RiL helth sgent

A ret yhdudhen rdnl eelorte.

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Slide text:

Robustness and Ablation

What produced reliability?

1.RL:success matched the

deterministic pol 100± 30 synthetic seeds; mean ± 95% Cl

2.Deterministic rules beat random percentage-point'difference -15.03 ± 0.66 pp

selection by.15:63 ±'1.86 points Safety override

3.The safety override reduced vs none +0.00 ±:0.19 pp

4.RL provided an inspectable learned policy; butdid not points non-escalation by.15:03:± 0.66 Rules vs random RL vs rules +15.63 ± 1.86 pp

outperform.rules in this -15 -10 -5 0 5 10 15

icnmg Difference (percentage points)

5.Structured decision'logic and

external guardrails produced

nost of the reliability

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Slide text:

What This Prototype Does Not Yet Prove

1. Results are based on synthetic benchmark scenarios

2. The agent is failure-triggered, not a pre-failure predictor

3. Production incident diversity may exceed the current state space

4. Some remediation actions remain simulated or bounded

5. Online learning requires strict operational approval gates

This demonstrates feasibility and system design, not production completeness.

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Slide text:

Small AI Can Still Deliver Operational Value

1. Use deterministic logic for observable facts

2. Use RL where contextual action selection adds value

3. Place safety constraints outside the learned policy

4. Treat escalation and validation as core capabilities

5. Evaluate across repeated seeds, not one favorable run

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Slide text:

From reactive debugging to intelligent recovery!

The goal is not to replace human judgment.

It is to reserve it for the failures that truly need it.

Before:

- Manual log inspection

- Schema tracing

- Delayed dashboards

- Modeled MTTR: 2.5 working days

After:

- Event-triggered diagnosis

- RL-guided remediation

- Safety-constrained escalation

- Minutes-scale recovery

Classification audit: raw/sources/slide-ai-classification/slides/LrGCT7G_rU8/audit.json

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