[Feature] Agentic Subprocess History

### Description of Problem

As part of looking at an Eval framework for agentic processes there is a requirement to be able to gather the data from an agentic process. This data can then be used to evaluate the performance and accuracy of the agentic process facilitating EDD.

### Potential Solutions

Proposal to add a dedicated history layer to the agentic subprocess plugin, enabling users to retrieve a full
history of every agent subprocess execution, including LLM calls, tool invocations, iteration counts, token usage, and final output. This can then be collated as a single json response through a dedicated REST endpoint. The implementation is entirely self-contained within the plugin; no changes to the main `fluxnova-bpm-platform` repository are required.

Suggested new Events:

### `agent-subprocess` entity

| Constant | Entity Type | Event Name | Fired When |
|--------------------------|--------------------|------------|-----------------------------------------------------------------------------------------------|
| `AGENT_SUBPROCESS_START` | `agent-subprocess` | `start` | The agent subprocess execution is entered and the first orchestration job is scheduled |
| `AGENT_SUBPROCESS_END` | `agent-subprocess` | `end` | The subprocess terminates (LLM returned a text-only response or the tool catalogue was empty) |

### `agent-loop` entity

| Constant | Entity Type | Event Name | Fired When |
|--------------------|--------------|------------|-----------------------------------------------------------------------------------|
| `AGENT_LOOP_START` | `agent-loop` | `start` | Each orchestration job begins executing (i.e. each LLM round-trip cycle starts) |
| `AGENT_LOOP_END` | `agent-loop` | `end` | The LLM response for that cycle has been processed and the next action determined |

### `agent-llm` entity

| Constant | Entity Type | Event Name | Fired When |
|----------------------|-------------|------------|----------------------------------------------------------------------------------------------------------------------------|
| `AGENT_LLM_REQUEST` | `agent-llm` | `request` | Immediately before calling `LlmService.call(...)` — captures the outbound prompt token count and message count |
| `AGENT_LLM_RESPONSE` | `agent-llm` | `response` | Immediately after the `LlmResponse` is received — captures token usage and whether the response was `TEXT` or `TOOL_CALLS` |

### `agent-tool-call` entity

| Constant | Entity Type | Event Name | Fired When |
|-----------------------------|-------------------|-------------|--------------------------------------------------------------------------------------------------------------------------|
| `AGENT_TOOL_CALL_REQUESTED` | `agent-tool-call` | `requested` | The LLM has requested a tool call and it has been validated against the catalogue, before `triggerAdHocActivities` fires |
| `AGENT_TOOL_CALL_COMPLETED` | `agent-tool-call` | `completed` | `SubprocessToolCompletionListener` confirms the tool activity ended successfully |
| `AGENT_TOOL_CALL_FAILED` | `agent-tool-call` | `failed` | The tool activity ended in error (the result is still fed back to the LLM as an error message) |

Suggested endpoint response would look like:

```json

{
"subprocessExecutionId": "8f3a1c92-...",
"processInstanceId": "d4b72e11-...",
"processDefinitionKey": "riskReportProcess",
"elementId": "agentScope",
"provider": "ollama",
"model": "llama3",
"goal": "Generate risk report",
"finalOutput": "The risk report for Q3 has been generated and stored.",
"iterations": 4,
"totalPromptTokens": 3820,
"totalCompletionTokens": 614,
"executionTime": 4200,
"startTime": "2026-08-05T13:00:00.000Z",
"endTime": "2026-08-05T13:00:04.200Z",
"toolCalls": [
{
"toolCallId": "call_abc123",
"toolName": "Fetch market data",
"toolElementId": "fetchMarketData",
"loopIndex": 1,
"requestedAt": "2026-08-05T13:00:00.950Z",
"completedAt": "2026-08-05T13:00:01.184Z",
"durationMs": 234,
"status": "COMPLETED",
"errorMessage": null
}
"..."
],
"step-history": [
{
"type": "agent-subprocess:start",
"timestamp": "2026-08-05T13:00:00.000Z",
"elementId": "agentScope",
"provider": "ollama",
"model": "llama3"
},
{
"type": "agent-loop:start",
"timestamp": "2026-08-05T13:00:00.050Z",
"loopIndex": 1
},
"..."
]
}
```