From incident alert to governed fix — in under five minutes.

The eight steps below represent the actual sequence the Oracle AI platform executes for every incident. We use ORA-01652, a TEMP tablespace exhaustion error, as the concrete example throughout.

ORA-01652: Invoices Stuck in INCOMPLETE

ORA-01652 fires when Oracle cannot extend a temporary segment because the TEMP tablespace has run out of space. In an AP environment, this surfaces as invoices stuck in INCOMPLETE status — blocking payment processing, GL transfer, and period close. The platform handles the entire resolution sequence from alert to fix.

Incident Parameters
Error Code
ORA-01652
Module
Accounts Payable
Symptom
Invoices stuck in INCOMPLETE
Affected Records
50 invoices blocked
Risk Classification
🔴 High — admin approval required
Resolution Time
< 5 minutes end-to-end
Oracle AI Platform 8-Step Incident Flow — the complete sequence from Incident Submitted through Knowledge Stored with Human Approval Gate at Step 6

The complete 8-step sequence at a glance. Scroll down for the full technical detail of each step.

01
User → Support Portal

Incident Submitted

A user submits "Invoices stuck in status INCOMPLETE with ORA-01652" through the Oracle AI support portal. The portal captures the error code, module, description, and Oracle environment version as a structured JSON payload sent to an n8n webhook listener.

Webhook Payload
{
  "error_code": "ORA-01652",
  "module": "AP",
  "description": "Invoices stuck in INCOMPLETE status",
  "environment": "EBS R12.2.10",
  "priority": "HIGH"
}
02
🟢 Low Risk — Autonomous
n8n Webhook → LLM Router

Triage & Classification

The n8n webhook receives the JSON payload and passes it to an LLM-based router agent. The agent evaluates the error code, module, and description to classify the incident domain — in this case, a DBA-level tablespace issue — and dispatches the correct diagnostic workflow branch. Classification determines which diagnostic scripts run, which Oracle Support search terms are used, and which approval tier applies to the fix.

03
🟢 Low Risk — Autonomous
Script Agent → Oracle Database

Diagnostic Scripts Execute

n8n triggers the Tablespace Diagnostic Script from the 65-script library at scripts.williamagreen.com. The script executes via the n8n OracleDB node using JDBC, running structured SQL checks against the live database and returning results as normalized JSON for LLM analysis.

SYS-03: Tablespace Diagnostic
SELECT tablespace_name,
       used_percent,
       bytes_used,
       bytes_free
FROM   dba_tablespace_usage_metrics
WHERE  tablespace_name = 'TEMP';
04
🟢 Low Risk — Autonomous
LLM Analyst — Claude / GPT-4o

Root Cause Identified

The structured diagnostic output is passed to the LLM analysis engine. The model identifies root cause based on specific patterns in the script output, and also consults the RAG knowledge base for prior resolutions matching this error code and environment.

LLM Analysis Output
Tablespace: TEMP  |  Used: 99.8%  |  Free: 40MB

Root cause: TEMP tablespace exhaustion.
50 AP invoices cannot complete sort operations.
Resolution: Extend TEMP by 5GB minimum.
05
🟢 Low Risk — Autonomous
Playwright Agent → My Oracle Support

Oracle Support Knowledge Retrieved

The Playwright browser agent activates using a pre-authenticated session stored in auth.json, navigates to My Oracle Support, searches "ORA-01652 TEMP Tablespace EBS R12", and extracts the Symptoms, Cause, and Solution sections from the top-ranked article. This retrieval runs in parallel with Step 4.

Session persistence: The agent uses Playwright's storageState feature to maintain the MOS authentication session across runs, avoiding MFA challenges on every invocation. Session expiry triggers a human re-authentication alert.
06
🔴 High Risk — Admin Approval Required
HITL Node → Lead DBA via Slack

Human Approval Required

Because extending a tablespace requires an ALTER SYSTEM command — a High-Risk DDL-level action — the platform executes its Safe-Stop sequence. n8n serializes the full proposed fix context and sends a Decision Tower notification to the lead DBA via Slack. The notification presents three panels of evidence: the problem, the AI reasoning path, and the dry-run result. The DBA sees Approve, Reject, and Edit SQL buttons directly in the Slack message.

Safe-Stop guarantee: If the DBA does not respond, nothing executes. The platform holds state indefinitely. There is no timeout path that executes a high-risk fix without a human signature. This is architectural, not configurable.
Decision Tower Slack notification showing ORA-01652 fix approval request with Approve Fix, Reject, and Edit SQL buttons

The Decision Tower notification as it appears in Slack — three evidence panels and three action buttons. The DBA's entire involvement is reviewing this card and clicking one button.

07
🔴 High Risk — Executed After Approval
n8n → OracleDB Node

Fix Executed & Logged

Upon DBA approval, n8n validates the JWT authorization token and executes the fix via the OracleDB node using the exact SQL that was shown in the approval notification — no substitution, no modification. A signed audit record is written immediately after execution, capturing agent identity, approver identity, exact SQL run, and the before/after tablespace state.

Executed Fix
ALTER DATABASE
  TEMPFILE '/oracle/oradata/temp01.dbf'
  RESIZE 10240M;
08
🟢 Low Risk — Autonomous
RAG Pipeline → Milvus

Resolution Stored for Next Time

The complete resolution package — diagnostic output, root cause analysis, Oracle Support Note reference, fix applied, DBA approval record, and verification result — is vectorized and stored in Milvus with enriched metadata. The next ORA-01652 incident in this environment retrieves this resolution immediately, reducing diagnosis time to near zero.

Compound value: The first ORA-01652 resolution takes under 5 minutes. The tenth resolution, with a matching historical case in the knowledge base, takes under 60 seconds. The platform gets faster as it learns.

Common Questions

What happens if the AI misidentifies the root cause?

For High-Risk fixes, the DBA reviews the AI reasoning before approving any action. The Decision Tower UI shows exactly what evidence the AI used and what SQL it proposes to run. A misidentified root cause results in a Reject decision — not an erroneous fix. For Low-Risk diagnostic actions, a wrong analysis produces a wrong report, which the user can flag to trigger a re-run.

Can the platform handle Oracle Fusion as well as EBS?

Yes. For Oracle EBS R12.x, the platform connects via JDBC and SQL*Plus for direct database diagnostics. For Oracle Fusion Cloud, the platform uses Fusion REST APIs, OTBI logical SQL, BIP data models, and ESS job history endpoints — the same access methods documented across the 15 Fusion-specific scripts at scripts.williamagreen.com.

How is the platform deployed in a corporate network environment?

n8n is self-hosted via Docker, deployed within or connected to the corporate network that reaches the Oracle EBS instances. The Playwright browser agent runs in a separate container. Oracle credentials are stored in n8n's encrypted credential vault or HashiCorp Vault — never hardcoded in any script or workflow definition.

What Oracle modules does the platform currently cover?

The diagnostic script foundation covers 11 Oracle modules: Accounts Payable, Accounts Receivable, General Ledger, Fixed Assets, Purchasing, Inventory, Order Management, WIP/Manufacturing, Project Accounting, HCM/Payroll, and System/Technical. All 65 scripts are documented at scripts.williamagreen.com.

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