Template for agent orchestration and security. Enables agent2agent workflows, mechanistic interpretability, and integration via DLL injection and CLI wrappers. Benefits operations teams managing AI agents.
git clone https://github.com/AndrewAltimit/template-repo.gitTemplate for agent orchestration and security. Enables agent2agent workflows, mechanistic interpretability, and integration via DLL injection and CLI wrappers. Benefits operations teams managing AI agents.
1. **Set Up Dependencies:** Clone template-repo and install dependencies: `pip install -r requirements.txt` and `git submodule update --init`. Ensure Python 3.10+ and Docker (for DLL injection testing) are installed. 2. **Configure Agent2Agent:** Edit `config/agent_communication.yaml` to specify protocols (e.g., gRPC, REST) and ports. Use `template-repo/scripts/test_communication.py` to validate connectivity. 3. **Inject Security Modules:** Place DLLs in `security_modules/` and update `config/security_policies.json` to whitelist modules. Test with `template-repo/scripts/inject_dll.py --agent agent_b --module auth_check.dll`. 4. **Enable Mechanistic Interpretability:** Enable logging in `config/interpretability.json` and run `template-repo/scripts/generate_interpretability_report.py --agent agent_c --output report.json`. 5. **Deploy CLI Wrappers:** Symlink wrappers to `/usr/local/bin/` (e.g., `ln -s template-repo/wrappers/process_query /usr/local/bin/process_query`). Use `--help` flags to verify functionality.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/AndrewAltimit/template-repoCopy the install command above and run it in your terminal.
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Design a secure agent orchestration workflow for [AGENT_TYPE] using template-repo. Include steps for: 1) Setting up agent2agent communication via [PROTOCOL], 2) Implementing mechanistic interpretability checks for [AGENT_BEHAVIOR], 3) Integrating security policies via DLL injection for [SECURITY_MODULE], and 4) Exposing CLI wrappers for [OPERATIONS_TASK]. Provide a diagram of the workflow architecture.
### Secure Agent Orchestration Workflow for Customer Support Agent
**Workflow Architecture:**
```
[Customer Query] → [CLI Wrapper: 'process_query'] → [Agent A: Intent Recognition]
↓
[Agent B: Security Validator (DLL Injection: 'auth_check.dll')]
↓
[Agent C: Knowledge Retrieval (Mechanistic Interpretability: 'retrieval_analysis.json')]
↓
[Agent D: Response Generation] → [CLI Wrapper: 'format_response'] → [Customer]
```
**Detailed Steps:**
1. **Agent2Agent Communication:** Configured using gRPC (port 50051) with TLS encryption. Agent A (intent recognition) sends structured JSON to Agent B (security validator) via bidirectional streaming.
2. **Mechanistic Interpretability:** Agent C logs retrieval patterns to `retrieval_analysis.json`. A weekly report highlights that 12% of queries trigger the 'high_risk' flag due to ambiguous intent, prompting a retraining cycle for Agent A.
3. **Security Integration:** The `auth_check.dll` module is injected into Agent B’s process space. It validates user permissions against Active Directory and blocks 8% of queries from unregistered devices (e.g., query from 'device-123' rejected with error code 403).
4. **CLI Wrappers:** The `process_query` CLI accepts `--priority high` flags and routes to Agent A’s queue. The `format_response` CLI enforces output sanitization (e.g., removes profanity via regex) and logs response time metrics.
**Security Policies Enforced:**
- All inter-agent traffic encrypted (AES-256).
- DLL injection restricted to whitelisted modules (e.g., `auth_check.dll`, `rate_limiter.dll`).
- CLI wrappers log all inputs/outputs to `/var/log/agent_orchestrator/audit.log`.Cloud ETL platform for non-technical data integration
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