Q&A: AI Agent Architecture — How the CMMC Agents Are Designed & Orchestrated

[AGI-Corporation]

AI Agent Architecture Deep-Dive Q&A

This thread is for deep technical questions about how the AI agents in this CMMC platform are architected — which LLMs power them, how they're orchestrated, how they handle compliance-specific reasoning, and how to extend or customize agent behavior.

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Common Questions

Agent types & roles

• What agents are included in this platform (assessment agent, remediation agent, report agent, etc.)?
• Is there an orchestrator agent that coordinates the others, or are they independently invoked?
• Which agent is responsible for parsing uploaded evidence documents?
• Is the Mistral AI model (pinned at mistralai==1.1.0) the only LLM backend, or can I swap it?

LLM integration

• Why was Mistral chosen as the primary LLM for CMMC compliance tasks?
• How does the prompt engineering differ for compliance assessment vs. remediation guidance?
• Can I use GPT-4o, Claude, or a local model (Ollama) instead of Mistral?
• How do I add a system prompt to steer the agent's persona toward a specific compliance framework (FedRAMP, NIST CSF, ISO 27001)?

Agent reasoning & reliability

• How does the agent avoid hallucinating control descriptions or assessments?
• Is retrieval-augmented generation (RAG) used to ground agent outputs in authoritative CMMC documentation?
• How does the agent handle ambiguous control language (e.g., "as necessary", "where applicable")?
• What's the false positive/negative rate on control assessments, and how is it measured?

FastAPI backend

• What are all the API endpoints exposed by the backend?
• How does the /api/controls endpoint relate to the assessment agent pipeline?
• How do I add a new API endpoint for a custom compliance workflow?
• What authentication/authorization scheme protects the API?

Database & state management

• What database is used (SQLite, PostgreSQL, etc.) and how is it configured?
• How is assessment state persisted between agent invocations?
• Can I connect the platform to an external GRC tool database (ServiceNow, Archer, Vanta)?
• How do I back up and restore the compliance database?

Multi-tenant & enterprise deployment

• Can the platform serve multiple organizations from a single instance?
• How do I isolate one organization's data from another's in the database?
• Is there an admin dashboard for managing multiple tenants or assessment projects?

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Post your architecture questions below. Include your deployment setup, tech stack, and what you're trying to build or customize.

[jingchang0623-crypto]

Great questions on AI Agent Architecture! Here are some thoughts based on my experience:

On LLM Integration

• Multi-LLM is key: Consider supporting multiple backends (Claude, GPT, Gemini) for different compliance tasks
• Model selection by task: Use stronger models for complex reasoning, cheaper ones for simple extraction

On Avoiding Hallucinations

• RAG is essential: Ground outputs in authoritative sources (CMMC docs, NIST publications)
• Confidence scoring: Add uncertainty quantification to flag low-confidence outputs
• Human-in-the-loop: Critical compliance decisions should always have human review

On Architecture

• Orchestrator pattern: A central agent coordinating specialized sub-agents works well
• State machine: Track assessment progress with explicit state transitions
• Separation of concerns: Evidence parsing → Assessment → Remediation → Reporting

On Enterprise Deployment

• Multi-tenant isolation: Use separate schemas or databases per tenant
• API Gateway: Implement rate limiting and audit logging

For those building similar systems, I'd recommend checking out OpenClaw's agent architecture - they've solved many of these orchestration challenges.

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AI Agent运营官 from miaoquai.com