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---
name: agent-observatory-workflow
description: >-
Step-by-step instructions for extending the Python AI Observatory agent: adding LangChain tools,
Tool-Calling RAG workflows, enforcing Human-in-the-Loop approvals, multi-key OpenRouter failover, and pgvector embeddings.
---
# Agent Observatory Workflow & Extension Guide
This skill guides agents and engineers on how to safely build, modify, test, and enhance AI agent features within `agentic-observatory/`.
## 1. Branch-First Development
> [!IMPORTANT]
> **CREATE A LOCAL BRANCH FIRST**: Always start by creating a local branch from `main`:
> ```bash
> git switch -c MishraShardendu22/main/<feature-name>
> ```
> Never develop or modify agent code directly on `main`.
---
## 2. Adding a New Agent Tool
1. Create or update a tool file under [`agentic-observatory/data/tools/`](file:///home/ms22/Coding_stuff/Personal-Projects/github-backup-automation-system/agentic-observatory/data/tools/):
```python
from typing import Annotated, Any
from langchain_core.tools import tool
@tool
async def inspect_custom_metric(
metric_name: Annotated[str, "The name of the metric to query"],
days: Annotated[int, "Number of lookback days"] = 7,
) -> dict[str, Any]:
"""Query custom operational metrics from the database."""
# Perform database query or API call
return {"metric": metric_name, "value": 42}
```
2. Export the tool in [`agentic-observatory/data/tools/__init__.py`](file:///home/ms22/Coding_stuff/Personal-Projects/github-backup-automation-system/agentic-observatory/data/tools/__init__.py).
3. Add the tool to the `TOOLS` list in [`agentic-observatory/agent/openrouter.py`](file:///home/ms22/Coding_stuff/Personal-Projects/github-backup-automation-system/agentic-observatory/agent/openrouter.py).
---
## 2. Tool-Calling RAG & Vector Knowledge Base
The AI Observatory operates as a **Tool-Calling RAG Agent**:
1. **Pre-turn Retrieval**: Injects top relevance chunks into system context before iteration 1.
2. **Dynamic Tool Calling**: The agent calls `hybrid_search_knowledge_base` during reasoning loops for deep evidence gathering:
```python
# Inside agent/openrouter.py:
from data.tools import hybrid_search_knowledge_base
```
* Supported source filters: `['chat_message', 'execution_log', 'investigation', 'backup_result', 'backup_fix']`.
* Combines Full-Text Search (tsvector), pgvector cosine similarity, and Reciprocal Rank Fusion (RRF).
---
## 3. Implementing Human-In-The-Loop (HITL) Actions
For sensitive actions (e.g., sending emails, applying hotfixes, modifying DB records):
1. In `agentic-observatory/agent/openrouter.py`, intercept the tool before execution:
```python
if tool_name == "send_report_email":
confirm_id = str(uuid.uuid4())
confirm_event = asyncio.Event()
active_confirmations[confirm_id] = confirm_event
yield json.dumps({
"type": "confirm_required",
"confirm_id": confirm_id,
"name": tool_name,
"args": tool_args,
})
# Wait up to 120s for user response via /chat/confirm
await asyncio.wait_for(confirm_event.wait(), timeout=120.0)
```
2. Feed the user approval or rejection back to the LLM context.
---
## 4. Working with Multi-Key OpenRouter Failover
Always use [`agentic-observatory/utils/openrouter_keys.py`](file:///home/ms22/Coding_stuff/Personal-Projects/github-backup-automation-system/agentic-observatory/utils/openrouter_keys.py):
* `get_openrouter_api_keys()`: Returns all configured keys.
* `get_active_openrouter_key()`: Returns the currently active working key.
* `rotate_openrouter_key(failed_key, reason)`: Advances to the next backup key when an error (`401`, `402`, `429`) occurs.
---
## 5. Comprehensive Agent Test Suites
Run the test suite commands:
```bash
# 1. Run all unit and integration tests across the system
make test
# 2. Run dedicated AI Agent & Tool-Calling RAG test suite
make test-agents
# 3. Direct execution of Agent tests
cd agentic-observatory && uv run python test_agent_suite.py
```