feat(ai-insights): simplify & align manual instrumentation docs (#15774)

Author: obostjancic

develop-docs/sdk/telemetry/traces/modules/ai-agents.mdx

@@ -77,9 +77,9 @@ Additional attributes on the span:
 
 This span represents a request to an AI model or service that generates a response or requests a tool call based on the input prompt.
 
-- Span `op` MUST be `"gen_ai.{gen_ai.operation.name}"`. (e.g. `"gen_ai.chat"`)
+- Span `op` MUST be `"gen_ai.{gen_ai.operation.name}"`. (e.g. `"gen_ai.request"`)
 - Span `name` SHOULD be `{gen_ai.operation.name} {gen_ai.request.model}"`. (e.g. `"chat o3-mini"`)
-- Attribute `gen_ai.operation.name` MUST be the operation performed.[3] (e.g. `"chat"`.)
+- Attribute `gen_ai.operation.name` MUST be the operation performed.[3] (e.g. `"summarize"`.)
 - All [Common Span Attributes](#common-span-attributes) SHOULD be set (all `required` common attributes MUST be set).
 
 Additional attributes on the span:
@@ -154,7 +154,7 @@ Some attributes are common to all AI Agents spans:
 
 | Attribute               | Type   | Requirement Level | Description                                                                              | Example               |
 | :---------------------- | :----- | :---------------- | :--------------------------------------------------------------------------------------- | :-------------------- |
-| `gen_ai.operation.name` | string | required          | The name of the operation being performed. **[2]**                                       | `"chat"`              |
+| `gen_ai.operation.name` | string | required          | The name of the operation being performed. **[2]**                                       | `"summarize"`         |
 | `gen_ai.request.model`  | string | required          | The name of the AI model a request is being made to.                                     | `"gpt-4o"`            |
 | `gen_ai.response.model` | string | optional          | The name of the AI model the response was created with.                                  | `"gpt-4o-2024-08-06"` |
 | `gen_ai.agent.name`     | string | optional          | The name of the agent this span belongs to.                                              | `"Weather Agent"`     |
@@ -164,7 +164,7 @@ Some attributes are common to all AI Agents spans:
 
 | Value                | Description                                                             |
 | :------------------- | :---------------------------------------------------------------------- |
-| `"chat"`             | Chat completion operation such as OpenAI Chat API                       |
+| `"summarize"`        | Chat completion operation such as OpenAI Chat API                       |
 | `"responses"`        | OpenAI Responses API                                                    |
 | `"create_agent"`     | Create GenAI agent                                                      |
 | `"embeddings"`       | Embeddings operation such as OpenAI Create embeddings API               |
@@ -193,5 +193,7 @@ Some attributes are common to all AI Agents spans:
 | `"xai"`             | xAI                               |
 
 ## Size Limits
+
 In addition to the [general size limits](https://docs.sentry.io/concepts/data-management/size-limits/) for events, spans, and attachments, there are specific size limits for AI integrations:
-- Input messages in AI integrations recorded in the span attribute `gen_ai.request.messages` are limited to 20kB per span. Messages are trimmed and truncated if they exceed the limit, retaining the most recent messages.
+
+- Input messages in AI integrations recorded in the span attribute `gen_ai.request.messages` are limited to 20kB per span. Messages are trimmed and truncated if they exceed the limit, retaining the most recent messages.

docs/platforms/javascript/common/configuration/integrations/langchain.mdx

@@ -144,7 +144,7 @@ Sentry.init({
 
 By default this integration adds tracing support for LangChain operations including:
 
-- **Chat model invocations** (`gen_ai.chat`) - Captures spans for chat model calls
+- **Chat model invocations** (`gen_ai.request`) - Captures spans for chat model calls
 - **LLM invocations** (`gen_ai.pipeline`) - Captures spans for LLM pipeline executions
 - **Chain executions** (`gen_ai.invoke_agent`) - Captures spans for chain invocations
 - **Tool executions** (`gen_ai.execute_tool`) - Captures spans for tool calls

docs/platforms/javascript/common/tracing/instrumentation/ai-agents-module-browser.mdx

@@ -39,7 +39,7 @@ notSupported:
 
 With <Link to="/product/insights/ai/agents/dashboard/">Sentry AI Agent Monitoring</Link>, you can monitor and debug your AI systems with full-stack context. You'll be able to track key insights like token usage, latency, tool usage, and error rates. AI Agent Monitoring data will be fully connected to your other Sentry data like logs, errors, and traces.
 
-As a prerequisite to setting up AI Agent Monitoring in browser applications, you'll need to first <PlatformLink to="/tracing/">set up tracing</PlatformLink>. 
+As a prerequisite to setting up AI Agent Monitoring in browser applications, you'll need to first <PlatformLink to="/tracing/">set up tracing</PlatformLink>.
 
 <Alert level="info">
 
@@ -108,7 +108,6 @@ Sentry.startSpan(
     name: `invoke_agent ${myAgent.name}`,
     attributes: {
       "gen_ai.operation.name": "invoke_agent",
-      "gen_ai.system": myAgent.modelProvider,
       "gen_ai.request.model": myAgent.model,
       "gen_ai.agent.name": myAgent.name,
     },
@@ -168,7 +167,6 @@ Sentry.startSpan(
     name: `chat ${myAi.model}`,
     attributes: {
       "gen_ai.operation.name": "chat",
-      "gen_ai.system": myAi.modelProvider,
       "gen_ai.request.model": myAi.model,
     },
   },
@@ -253,7 +251,6 @@ if (result.toolCalls && result.toolCalls.length > 0) {
       op: "gen_ai.execute_tool",
       name: `execute_tool ${tool.name}`,
       attributes: {
-        "gen_ai.system": myAi.modelProvider,
         "gen_ai.request.model": myAi.model,
         "gen_ai.tool.type": "function",
         "gen_ai.tool.name": tool.name,
@@ -322,7 +319,6 @@ if (result.handoffTo) {
       op: "gen_ai.handoff",
       name: `handoff from ${myAgent.name} to ${otherAgent.name}`,
       attributes: {
-        "gen_ai.system": myAgent.modelProvider,
         "gen_ai.request.model": myAgent.model,
       },
     },