Agentic Control Plane (ACP)

Rajmohan Mangattu | CCIE Collaboration #55207

LAB PROTOTYPE — built to demonstrate governed agentic AI architecture at enterprise scale, combining years of Cisco UC/CC/networking expertise with modern AI engineering.

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What this is

The Agentic Control Plane (ACP) is a governed MCP (Model Context Protocol) server that sits between AI agents and domain knowledge — enforcing OAuth 2.1 scope checking, policy-driven access control, and a full audit trail on every tool call.

It is not a chatbot. It is the governance layer that enterprise AI deployments need before autonomous agents can be trusted in regulated industries.

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The A2A boundary — what this demonstrates

Natural language question
        ↓
network_caller.py (MCP client)
  — grounds LLM with real inventory
  — Groq picks tool + parameters
  — validates against real topology
        ↓  real MCP protocol over HTTP
ACP on port 8100 (MCP server)
  — decodes JWT token
  — checks control_hub.yaml policy
  — runs domain tool
  — writes audit line
        ↓
Prose answer + governed audit trail

Demo run:

Question : "What is the device in Mumbai branch?"
Groq picks: check_device_role(node=branch-cedge-01)
ACP says  : ALLOWED — governance passed
Answer    : Cisco ISR1100 IOS-XE SD-WAN cEdge, spoke role,
            5 peers, dual-transport MPLS + internet
Audit     : tool=check_device_role decision=ALLOWED written to audit.readable.log

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Demo — the A2A boundary in action

A natural-language question about a Cisco SD-WAN network, answered end to end through the governed MCP boundary.

1. ACP server running

The governed MCP server starts and serves both /health and /mcp on port 8100.

2. The A2A flow — natural language to governed tool call

The caller grounds the LLM with real inventory, Groq picks find_path with the correct parameters, the code validates them against the real topology, then calls ACP over the real MCP protocol.

3. The governed answer

Groq composes a prose answer from the governed result — every fact traced to the topology, crossing the SD-WAN OMP tunnel.

4. Governance blocks an unauthorized caller

The same question with a viewers token. Governance blocks the flow at the first tool call — no inventory returned, no answer, distinct audit reason.

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Architecture

┌─────────────────────────────────────────────────────────┐
│  network_caller.py — A2A caller                         │
│  Groq tool router + validation guard + prose writer     │
└──────────────────────────┬──────────────────────────────┘
                           │ MCP protocol (HTTP port 8100)
                           ▼
┌─────────────────────────────────────────────────────────┐
│  ACP governed MCP server                                │
│  ┌──────────────────┬──────────────────────────────┐   │
│  │ Track C          │ Track N                       │   │
│  │ search_wxcc_     │ describe_topology             │   │
│  │ corpus           │ find_path                     │   │
│  │                  │ check_device_role             │   │
│  └──────────────────┴──────────────────────────────┘   │
│  OAuth 2.1 scopes + control_hub.yaml + audit trail      │
└─────────────────────────────────────────────────────────┘

Two domain modules

Track C — Contact Center Searches a corpus of 2,633 chunks of Cisco Webex Contact Center knowledge (BGE-M3/1024-dim embeddings, Qdrant Cloud). Returns Tier 1 sourced, grounded answers to specific deployment, compliance, and design questions — with provenance tracking so every answer traces back to its source document.

Track N — Network Three tools over a Cisco SD-WAN topology descriptor (9 nodes, 11 links):

• describe_topology — nodes, links, sites, SD-WAN hub/spoke summary
• find_path — BFS shortest L3 hop path between any two nodes
• check_device_role — role, platform, and peer connections for a named node

Governance on every call

Every tool call goes through two checks before the domain function runs:

1. Group policy — is this tool Allowed or Blocked for the caller's group in control_hub.yaml?
2. Scope check — does the JWT token carry the required scope (knowledge:read or diagnostics:run)?

Both refusal types write distinct audit lines to audit.readable.log with the reason. Allowed calls write an ALLOWED line. Nothing reaches the domain tool without passing both checks.

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Key design decisions

ACP has its own direct path to the data Rather than routing through the WxCC SLM's API, ACP keeps its own database client and embedder. This means the two systems are independent — if the SLM goes down, ACP still works. They share the data, not the code.

Governance is a parameter, not a dependency The governance function is passed into each tool as an argument rather than imported directly. This keeps the code clean and avoids circular dependencies — the tool doesn't need to know anything about the server that hosts it.

The LLM proposes, the code decides When the A2A caller asks Groq to pick a tool parameter (like a node name), it doesn't just trust the answer blindly. Every parameter is checked against the real data before any call goes to ACP. If the LLM guesses a node that doesn't exist, the caller rejects it and shows what's actually available.

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How to run

Prerequisites

• Python 3.11 (ACP venv)
• Qdrant Cloud account — collection wxcc_slm_corpus
• Groq API key — https://console.groq.com/keys
• BGE-M3 cached at D:\\hf_cache (or set HF_HOME to your cache path)

Setup

git clone https://github.com/Rajmohan80/project-acp.git
cd project-acp
python -m venv .venv
.venv\\Scripts\\activate
pip install -e .
cp .env.example .env
# Fill in .env with your real values

Run the A2A demo (two terminals)

Terminal 1 — start ACP server:

python scripts\\run_mcp_server.py

Wait for: Uvicorn running on http://0.0.0.0:8100

Terminal 2 — run the A2A caller:

python scripts\\network_caller.py --question "What is the device in Mumbai branch?"

Try --group viewers to see governance block the flow.

Run the WxCC corpus tool demo

python scripts\\demo_wxcc.py --query "What are the WxCC data residency requirements for UAE?"

Run the Track N topology tools demo

python scripts\\demo_network.py

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Repository structure

src/
  core/
    mcp/
      server/app.py          — governed MCP server, all tools registered
      control_hub.yaml       — allow/block policy per group per tool
      oauth/issuer.py        — JWT token minter (port 9000)
    audit/writer.py          — every decision → audit.readable.log
    common/config.py         — settings from .env, fails loud on missing vars
  domains/
    contact_center/
      corpus_client.py       — BGE-M3 + Qdrant client (Track C)
      tool.py                — search_wxcc_corpus governed tool
    network/
      topology_store.py      — JSON topology loader, BFS path finder
      tool.py                — describe_topology, find_path, check_device_role
      topologies/
        sample_sdwan_branch.json  — 9-node SD-WAN topology descriptor
scripts/
  run_mcp_server.py          — starts ACP as standing HTTP service
  network_caller.py          — A2A caller (grounded LLM + MCP client)
  demo_wxcc.py               — Track C validation
  demo_network.py            — Track N validation (6 runs)
docs/
  naming-map.md              — ACP ↔ Cisco concept mapping
  BLOCK_5A_MCP_TRANSPORT.md  — how the MCP HTTP transport was wired
  BLOCK_5_A2A_BOUNDARY.md    — A2A boundary design and validated output
  A2A_FLOW_DIAGRAM.md        — flow diagram + step-by-step explanation

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Governance proof — what the audit log shows

After running the demo, audit.readable.log contains entries like:

{"actor": "demo.engineer", "tool": "check_device_role",
 "outcome": "ALLOWED", "refusal_reason": "NONE", ...}

{"actor": "demo.viewer", "tool": "check_device_role",
 "outcome": "REFUSED", "refusal_reason": "TOOL_BLOCKED_IN_CONTROL_HUB", ...}

Two callers, same tool, different outcomes — based purely on the signed JWT token's group claim. The governance layer cannot be bypassed.

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Related projects

• wxcc-slm — the domain AI this project governs: https://github.com/Rajmohan80/wxcc-slm

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Stack

Component	Technology
MCP server	FastMCP 3.4.5 (streamable-http)
OAuth issuer	FastAPI + python-jose (JWT)
Policy enforcement	control_hub.yaml (YAML, no code change needed)
Audit trail	TinyDB → audit.readable.log
Embeddings	BGE-M3 (BAAI/bge-m3, 1024-dim, sentence-transformers)
Vector DB	Qdrant Cloud
LLM	Groq Llama-3.3-70B-versatile
Runtime	Python 3.11, uvicorn

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Rajmohan Mangattu | CCIE Collaboration #55207 Lab prototype — not production ready. Built to demonstrate governed agentic AI architecture for enterprise contact center and network domains.