docs(examples): fix doc link paths on agent-observability-docs.splunk.com (HYBIM-932 follow-up)

.github/workflows/ci-tests.yaml

@@ -52,11 +52,8 @@ jobs:
         run: |
           poetry env use "${{ steps.setup-python.outputs.python-path }}"
 
-      - name: Install invoke
-        run: pipx install invoke
-
       - name: Install Dependencies
-        run: invoke install
+        run: poetry install --all-extras --no-root
 
       - name: Verify Poetry Python version
         run: |
@@ -70,8 +67,8 @@ jobs:
 
       - name: Validate Types
         if: always()
-        run: invoke type-check
+        run: poetry run invoke type-check
 
       - name: Run Tests
         if: always()
-        run: invoke test-report-xml
+        run: poetry run invoke test-report-xml

examples/agent/crewAI/README.md

@@ -7,7 +7,7 @@ This repo contains examples of how to use Splunk AO to instrument [CrewAI](https
 The [resarch-crew](./research_crew/) is a quickstart tutorial. It is a completed version of the [CrewAI quickstart](https://docs.crewai.com/en/quickstart) and adds the
 Splunk AO's [CrewAIEventListener](https://agent-observability-docs.splunk.com/sdk-api/python/reference/handlers/crewai/handler),
 an event handler implemented on top of OpenTelemetry (OTel). For more information, see
-Splunk AO’s [Add Splunk AO to a CrewAI Application](https://agent-observability-docs.splunk.com/how-to-guides/third-party-integrations/add-galileo-to-crewai/add-galileo-to-crewai)
+Splunk AO’s [Add Splunk AO to a CrewAI Application](https://agent-observability-docs.splunk.com/how-to-guides/third-party-integrations/add-splunk-ao-to-crewai/add-splunk-ao-to-crewai)
 how-to guide.
 
 See the [README.md](./research_crew/README.md) for detailed setup instructions.

examples/agent/crewAI/research_crew/README.md

@@ -3,7 +3,7 @@
 This tutorial is a completed version of the [CrewAI quickstart](https://docs.crewai.com/en/quickstart) and adds the
 Splunk AO [CrewAIEventListener](https://agent-observability-docs.splunk.com/sdk-api/python/reference/handlers/crewai/handler),
 an event handler implemented on top of OpenTelemetry (OTel). For more information, see
-Splunk AO’s [Add Splunk AO to a CrewAI Application](https://agent-observability-docs.splunk.com/how-to-guides/third-party-integrations/add-galileo-to-crewai/add-galileo-to-crewai)
+Splunk AO’s [Add Splunk AO to a CrewAI Application](https://agent-observability-docs.splunk.com/how-to-guides/third-party-integrations/add-splunk-ao-to-crewai/add-splunk-ao-to-crewai)
 how-to guide.
 
 Below you'll find concise setup steps, configuration notes, and a short primer on Splunk AO so you know why we add it and what it provides.
@@ -12,7 +12,7 @@ Below you'll find concise setup steps, configuration notes, and a short primer o
 
 The relevant guides:
 
-- [Add Splunk AO to CrewAI](https://agent-observability-docs.splunk.com/how-to-guides/third-party-integrations/add-galileo-to-crewai/add-galileo-to-crewai)
+- [Add Splunk AO to CrewAI](https://agent-observability-docs.splunk.com/how-to-guides/third-party-integrations/add-splunk-ao-to-crewai/add-splunk-ao-to-crewai)
 - [CrewAI quickstart](https://docs.crewai.com/en/guides/crews/first-crew)
 
 ## What is Splunk AO?

examples/agent/langgraph-fsi-agent/README.md

@@ -172,7 +172,7 @@ From there you can configure the metrics you are interested in. Once metrics are
 
 This project also includes a unit test to run the chatbot with a set of defined prompts, evaluating the prompts for action advancement, action completion, tool selection quality, and tool errors, only passing the test if both metrics score an average of 100% (or 0% for tool errors) over all the entries in the dataset.
 
-This is run using the [Splunk AO experiments framework](https://agent-observability-docs.splunk.com/concepts/experiments/overview) - allowing you to run any code as an experiment against a fixed dataset of prompts. This mechanism allows you to run AI applications, from simple to complex, under test conditions with a defined set of inputs. You can then use the results of evaluations run against your app to help with model selection or prompt engineering, as well as validating your application as part of a CI/CD pipeline.
+This is run using the [Splunk AO experiments framework](https://agent-observability-docs.splunk.com/sdk-api/experiments/experiments) - allowing you to run any code as an experiment against a fixed dataset of prompts. This mechanism allows you to run AI applications, from simple to complex, under test conditions with a defined set of inputs. You can then use the results of evaluations run against your app to help with model selection or prompt engineering, as well as validating your application as part of a CI/CD pipeline.
 
 You can run the unit test by running the following command inside your virtual environment:
 

examples/chatbot/sample-project-chatbot/anthropic/README.md

@@ -70,7 +70,7 @@ These moons are significant for their unique geological features and potential f
 
 Every run of the app is logged as a new session in Splunk AO, with each prompt and response a separate trace.
 
-To evaluate the chatbot, head to your project and Log stream in the Splunk AO console. Turn on [instruction adherence](https://agent-observability-docs.splunk.com/concepts/metrics/response-quality/instruction-adherence) and [correctness](https://agent-observability-docs.splunk.com/concepts/metrics/response-quality/correctness) for your Log stream.
+To evaluate the chatbot, head to your project and Log stream in the Splunk AO console. Turn on [instruction adherence](https://agent-observability-docs.splunk.com/concepts/evaluators/response-quality/instruction-adherence) and [correctness](https://agent-observability-docs.splunk.com/concepts/evaluators/response-quality/correctness) for your Log stream.
 
 Then when you run the chatbot, these metrics will be evaluated.
 

examples/chatbot/sample-project-chatbot/azure-inference/README.md

@@ -71,7 +71,7 @@ These moons are significant for their unique geological features and potential f
 
 Every run of the app is logged as a new session in Splunk AO, with each prompt and response a separate trace.
 
-To evaluate the chatbot, head to your project and Log stream in the Splunk AO console. Turn on [instruction adherence](https://agent-observability-docs.splunk.com/concepts/metrics/response-quality/instruction-adherence) and [correctness](https://agent-observability-docs.splunk.com/concepts/metrics/response-quality/correctness) for your Log stream.
+To evaluate the chatbot, head to your project and Log stream in the Splunk AO console. Turn on [instruction adherence](https://agent-observability-docs.splunk.com/concepts/evaluators/response-quality/instruction-adherence) and [correctness](https://agent-observability-docs.splunk.com/concepts/evaluators/response-quality/correctness) for your Log stream.
 
 Then when you run the chatbot, these metrics will be evaluated.
 

examples/chatbot/sample-project-chatbot/openai-ollama/README.md

@@ -72,7 +72,7 @@ These moons are significant for their unique geological features and potential f
 
 Every run of the app is logged as a new session in Splunk AO, with each prompt and response a separate trace.
 
-To evaluate the chatbot, head to your project and Log stream in the Splunk AO console. Turn on [instruction adherence](https://agent-observability-docs.splunk.com/concepts/metrics/response-quality/instruction-adherence) and [correctness](https://agent-observability-docs.splunk.com/concepts/metrics/response-quality/correctness) for your Log stream.
+To evaluate the chatbot, head to your project and Log stream in the Splunk AO console. Turn on [instruction adherence](https://agent-observability-docs.splunk.com/concepts/evaluators/response-quality/instruction-adherence) and [correctness](https://agent-observability-docs.splunk.com/concepts/evaluators/response-quality/correctness) for your Log stream.
 
 Then when you run the chatbot, these metrics will be evaluated.
 

examples/experiments/multi-turn/README.md

@@ -69,7 +69,7 @@ Pre-defined session-level metrics include:
 - `SplunkAOMetrics.context_relevance`
 - `SplunkAOMetrics.tool_error_rate`
 
-Related documentation: [Metrics Comparison](https://agent-observability-docs.splunk.com/concepts/metrics/metric-comparison)
+Related documentation: [Metrics Comparison](https://agent-observability-docs.splunk.com/concepts/evaluators/evaluator-comparison)
 
 Optionally, you can define your own custom session-level metric in the Splunk AO Console UI, and then add the custom metric name. 
 

examples/experiments/upload_experiment/README.md

@@ -172,5 +172,5 @@ After running the script, your Splunk AO project will contain:
 
 - [Splunk AO Documentation](https://agent-observability-docs.splunk.com/what-is-splunk-agent-observability)
 - [Splunk AO SDK Reference](https://agent-observability-docs.splunk.com/sdk-api/overview)
-- [Creating Custom Metrics](https://agent-observability-docs.splunk.com/concepts/metrics/custom-metrics/custom-metrics-ui-llm)
+- [Creating Custom Metrics](https://agent-observability-docs.splunk.com/concepts/evaluators/custom-evaluators/custom-evaluators-ui-llm)
 - [Understanding Experiments](https://agent-observability-docs.splunk.com/sdk-api/experiments/experiments)

examples/logging-samples/log-mcp-calls/README.md

@@ -2,7 +2,7 @@
 
 This is an example project demonstrating how to use Splunk AO to log MCP server calls as tool spans. This code is described in the [Log MCP Server Tool Calls how-to guide](https://agent-observability-docs.splunk.com/how-to-guides/basics/log-mcp-server-calls/log-mcp-server-calls) in the Splunk AO documentation.
 
-The MCP server used by default is the [Splunk AO MCP server](https://agent-observability-docs.splunk.com/getting-started/mcp/setup-galileo-mcp), but you can use this code with any MCP server that supports a streamable HTTP connection by changing the `MCP_SERVER_URL` environment variable.
+The MCP server used by default is the [Splunk AO MCP server](https://agent-observability-docs.splunk.com/getting-started/mcp/setup-splunk-ao-mcp), but you can use this code with any MCP server that supports a streamable HTTP connection by changing the `MCP_SERVER_URL` environment variable.
 
 ## Getting Started