When an Oracle AI agent does not complete a task, first identify the product and the stage that failed. OCI Generative AI Agents and Oracle Integration 3 Agentic AI have different consoles, logs, limits, URLs and known issues. Record whether the error occurs during invocation, tool execution, retrieval or ingestion, or final response, then use the evidence surface for that product instead of changing the prompt at random.
Capture the exact error or HTTP status, region, deployment or release version, UTC timestamp, resource identifiers and request or interaction ID. Those details let you match the failure to a work request, status log, application log or documented issue.
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Identify the Oracle agent and the failing stage
“Oracle AI agent” is not one troubleshooting surface. Begin by confirming which service is running the task:
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- Oracle Integration 3 Agentic AI: investigate the integration instance, project deployment, agent activation, MCP invocation path, prompt-template version, conversation identifiers, RAG authentication and knowledge-base configuration.
Do not confuse either service with Oracle Cloud Agent, which manages virtual-machine operations rather than completing generative-AI tasks.
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| Failure stage | Typical symptom | First evidence to collect |
|---|---|---|
| Invocation | 404, authentication error, timeout or no agent response | Endpoint or MCP URL, HTTP status, request ID, region and UTC time |
| Tool execution | The agent starts but cannot query a database, call an API or write a result | Tool configuration, work-request details, IAM or database error |
| Retrieval or ingestion | The agent says information is unavailable or a knowledge-base job is incomplete | Ingestion-job status, file counts, file-level logs and RAG permissions |
| Final response | Tools run but the task ends with an error, empty result or truncation | Application log, message length, session details and the complete interaction trace |
Write down the deployment version and region before applying a workaround. Oracle limits and known issues can vary by release and region, and an issue documented for one product does not establish a cause in the other.
OCI Generative AI Agents: a step-by-step diagnosis
1. Inspect the agent, endpoint, tools and work requests
Open the agent’s details in the OCI console and review its configured endpoints and tools. Then open the associated work requests. A work request can show that the agent operation itself completed while a tool or endpoint failed, or that the provisioning operation never finished.
- Open the OCI Generative AI Agents resource in the correct compartment and region.
- Select the affected agent and inspect its endpoint, tool and knowledge-base configuration.
- Open the relevant work request and record its status, error message and log reference.
- Match the work-request time to the request or interaction ID from the failed task.
- Fix the specific resource reported by the work request, then retry with the same minimal reproduction.
Preserve the original error before editing a prompt or replacing a tool. Otherwise, a configuration change can remove the evidence needed to identify the first failure.
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2. Check knowledge-base ingestion evidence
If the task depends on documents, inspect the ingestion job rather than assuming the agent searched an empty index. The job view reports successful, failed and ignored file counts, duration and overall status. Its logs provide file-level outcomes and global failure messages; the related work request provides additional log and error details.
- Open the knowledge base and select the data source used by the agent.
- Open the ingestion job that ran before the failed interaction.
- Review the successful, failed and ignored counts and the job status.
- Open status logs and identify the individual file or global condition that failed.
- Correct the file, format, access or data-source cause reported in the log.
- Restart ingestion and confirm that the previously failed files become successful.
OCI skips files that were already ingested successfully. It retries failed files when those files have been updated, so changing an unaffected document will not necessarily cause the failed item to run again.
3. Repair a SQL tool that was created with an invalid schema
A SQL-tool creation request can appear successful while its work request contains a SQL or parser error. Oracle identifies an invalid database schema as the most common cause. The practical repair is to remove the failed tool and create it again with valid schema DDL.
- Open the SQL tool’s work request and read the parser or schema error.
- Validate the database schema DDL independently, including table names, column definitions, data types and required syntax.
- Delete the SQL tool that was created from the invalid schema.
- Create a new SQL tool using the corrected schema.
- Wait for the creation work request to finish successfully before invoking the agent.
Do not treat a successful API submission as proof that the tool is usable; the asynchronous work request is the authoritative evidence of whether the SQL definition was accepted.
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4. Fix missing files from a successful large query
For a large SQL query, OCI may place output in Object Storage rather than returning all rows inline. If the query succeeds but the expected output file is missing, verify both the selected bucket and the policies that allow the service to use it.
- Confirm that the bucket exists in the expected compartment and region.
- Check that the tool or agent is configured to use that exact bucket.
- Verify the required Object Storage IAM policies for writing and reading the result.
- Review the query work request and application log for an authorization or bucket-name error.
A valid SQL statement does not compensate for a nonexistent bucket or missing IAM permission.
Oracle Integration 3 Agentic AI: known failure paths
Incorrect MCP URL
Oracle documents a known issue in which the MCP URL displayed for an agent can be wrong. Construct the documented path using the project name:
/mcp-server/v1/projects/<ProjectName>/mcp
Substitute the deployed project name exactly, including capitalization where applicable, and test the complete invocation path. An incorrect path can return HTTP 404 even though Oracle still creates conversation or agent-instance records. Therefore, a newly created record does not prove that the request reached the intended agent.
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Agent activation after a project deployment
Exporting an agent and its integrations through a project deployment can leave the agent unable to activate on the destination instance. Oracle’s listed workaround is to create and activate a new agent on that destination instance rather than repeatedly retrying activation of the imported one.
- Confirm that the project deployment completed and that its integrations are present.
- Check the imported agent’s activation status and deployment error.
- If activation continues to fail, create a new agent in the destination instance.
- Configure the required integrations and knowledge-base references on the new agent.
- Activate the new agent and test it with a minimal request.
Prompt templates created before version 26.04
Oracle reports that prompt templates created before release 26.04 can import with extra characters. If an imported template produces malformed prompts or unexpected parsing, recreate the affected template on version 26.04 instead of trying to remove invisible characters manually. Verify the actual release deployed in the instance because the issue’s applicability depends on version.
Conversation IDs on older agents
For older agents that fail when a conversation ID is supplied, Oracle documents the conversationId query parameter. Check the invocation format expected by that agent version and include the parameter exactly as documented. If the endpoint still fails, test a new conversation without a reused identifier to separate an ID-format problem from an agent or integration problem.
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RAG REST authentication and knowledge-base settings
REST calls that perform retrieval-augmented-generation searches or ingestion require the authentication method and role-based access controls documented for the deployed integration. Check whether the call uses resource-owner-password credentials (ROPC) or client credentials, then verify that the calling identity has the required RBAC permissions.
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Also verify two Oracle Integration-specific settings:
- The knowledge-base connection name must be
OpenSearchConnection. - Review the documented behavior of the default-recipe knowledge base; its defaults can affect which connection and retrieval path are used.
Check these settings against the issue list for the exact Oracle Integration release. Known-issue applicability can change as the service is updated.
Check OCI service limits before changing the design
A valid prompt can still fail when the workload exceeds a service limit. Oracle’s limits documentation, updated August 26, 2026, lists these values; confirm the current console and region because quotas and regional tool caps can change.
| Limit | Documented value and qualification | What to check |
|---|---|---|
| Data-source file size | 100 MB maximum per file | Split or reduce oversized files before ingestion. |
| User message length | 24,000 characters maximum | Shorten the request or send information in multiple turns. |
| Active ingestion jobs | One active ingestion job per data source | Wait for the existing job to finish before starting another. |
| Tool count | 20 in Frankfurt, London and Chicago; 5 in Ashburn, Sao Paulo and Osaka, as listed in the 2026 documentation | Check the agent’s region and the current regional quota. |
| Other quotas | Agents, knowledge bases, endpoints, sessions and ingestion files have additional limits | Compare the workload with the current regional limits; some quotas may be increased by request, while preset limits cannot. |
Do not infer that a regional value applies globally. A tool-count limit documented for one set of regions may not apply to a newly available region or a different OCI edition.
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OCI Generative AI application logs are more useful than repeated prompt edits. In the console, filter logs by a narrow time range around a reproducible failure and select the relevant stream, including standard output or standard error. Open the selected log in OCI Logging when you need advanced filtering or correlation.
- Reproduce the failure once with a small, deterministic request.
- Set the log time range to begin shortly before the request and end shortly after it.
- Filter for the application stream and request or interaction identifier.
- Save the complete error context, including preceding warnings and the final status.
- Compare the log timestamp with the work request or endpoint response.
Keep the unedited log excerpt and redact secrets before sharing it. A single line such as “task failed” is rarely enough; the surrounding error often identifies a schema, permission, URL, quota or downstream-service problem.
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Choose the next check by symptom
| Observed symptom | Most useful next check | Likely boundary |
|---|---|---|
| OCI agent creation or update reports success, then fails in use | Open the associated work request and inspect tool or endpoint errors | Asynchronous resource provisioning |
| Knowledge-base answers omit recently added files | Review ingestion counts and file-level status logs | Ingestion or data-source access |
| SQL tool creation has a parser error | Validate schema DDL and recreate the tool | Database schema |
| Large query completes without an output file | Verify Object Storage bucket and IAM policies | Storage authorization |
| Oracle Integration invocation returns 404 | Construct the project MCP path and test the full URL | Invocation routing |
| Imported Oracle Integration agent will not activate | Create and activate a new agent on the destination instance | Project-deployment issue |
| Imported prompts contain unexpected characters | Recreate templates on version 26.04 | Release-specific template import |
| RAG REST call is unauthorized | Check ROPC or client-credentials use and RBAC permissions | Authentication and authorization |
Prevent false fixes and repeated failures
Change one boundary at a time. If you alter the prompt, schema, endpoint and IAM policy together, a successful retry will not reveal which change mattered. Save the failing request, then test a minimal request that exercises only the suspected component. For example, test an endpoint without tools, a tool with a simple query, or retrieval against one known-good document.
Keep configuration snapshots when possible: agent and endpoint identifiers, tool definitions, data-source names, bucket and compartment, integration release, prompt-template revision and authentication method. Compare a failing deployment with a working one in the same region. This often exposes a case-sensitive project name, a different connection name, a missing policy or a release mismatch that a generic retry hides.
Do not start multiple ingestion jobs for the same data source while diagnosing a stale answer. OCI permits one active ingestion job per data source, so concurrent retries can create misleading “already running” results. Likewise, do not resend a 24,000-character-plus message while testing an unrelated tool; first reduce the request so message-size rejection is not masking the tool error.
Build a reproducible escalation case
If the documented checks do not isolate the failure, send Oracle Support a compact, reproducible case rather than a general statement that the agent is broken. Include:
- The product name: OCI Generative AI Agents or Oracle Integration 3 Agentic AI.
- Region, compartment or integration instance, project and agent or endpoint identifiers.
- Deployment or release version.
- Exact UTC timestamp, HTTP status and request, interaction or conversation identifier.
- A redacted request that reproduces the problem and the expected result.
- Work-request status and error details, ingestion-job logs or application-log excerpts.
- Relevant database schema, Object Storage bucket and IAM boundary, with secrets removed.
- The shortest sequence of steps that consistently reproduces the failure.
Redact API keys, passwords, tokens, private document contents and personal data. Preserve resource names and timestamps where they are needed for Oracle to locate service-side records.
What a successful recovery looks like
After the fix, rerun the same minimal request and verify the complete path: invocation reaches the intended endpoint, required tools execute, retrieval returns the expected source, and the agent produces a final response. Then test the original workload, including its normal message size, document set, conversation behavior and output destination. This two-stage check prevents a workaround from appearing successful only because the test request was smaller or bypassed the failing integration.
Quick pre-retry checklist
- Is this OCI Generative AI Agents or Oracle Integration 3 Agentic AI?
- Did you record the region, release, UTC time and request or conversation ID?
- Does the failure occur at invocation, tool execution, ingestion, retrieval or response?
- Did you inspect the matching work request, ingestion status log or application log?
- Are file size, message length, active-job and regional tool limits satisfied?
- For SQL, is the schema valid and was the tool recreated after a parser failure?
- For large results, do the Object Storage bucket and IAM policies permit output?
- For Integration 3, is the project MCP path, connection name, authentication method and release-specific workaround correct?
Frequently Asked Questions
Why is my Oracle AI agent not completing the task?
First identify the product and failing stage. Match the exact error to an OCI work request, ingestion log or application log, or to the Oracle Integration invocation and known-issue path. A prompt change alone cannot fix an invalid schema, missing IAM permission, failed ingestion job, wrong MCP URL or quota violation.
Why does my OCI Generative AI Agent fail after creation succeeds?
The API submission can succeed while an asynchronous tool or endpoint operation fails. Open the agent’s work request, read its status and error details, and inspect the configured tool, endpoint and knowledge base. Retry only after the reported resource is corrected.
Why does an OCI knowledge-base agent ignore a document?
Review the relevant ingestion job’s successful, failed and ignored counts and file-level logs. Correct the reported file or access problem and restart ingestion. OCI skips files already ingested successfully and retries failed files after those files are updated.
Why does my SQL tool fail even though creation returned success?
Check the creation work request for a SQL or parser error. Oracle identifies an invalid database schema as the common cause. Validate the schema DDL, delete the failed tool and create it again with the corrected schema.
Why does my Oracle Integration AI agent return 404?
The displayed MCP URL may be incorrect. Use the documented path /mcp-server/v1/projects/<ProjectName>/mcp with the deployed project name, then test the full route. A 404 can occur even when conversation or agent-instance records are created.
What information should I send Oracle Support?
Provide the product, region, instance or compartment, project and agent identifiers, release, exact UTC time, HTTP status, request or conversation ID, redacted reproduction, work-request or log excerpts, and the relevant schema, bucket and IAM details without secrets.
The Bottom Line
Oracle AI-agent failures are product- and stage-specific. Identify the service, preserve the exact evidence, then follow the matching work request, ingestion, SQL, IAM, URL, release or limit check. Validate the complete path with a minimal reproduction before returning to the full workload.
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