Author: Akash Madan

Traditional orchestration relies on centralized control using one brain to manage the entire network.

Modern networks are organic with thousands of devices, millions of telemetry signals, and unpredictable traffic patterns.

Scaling intelligence doesn’t mean building a bigger brain. It means building many smaller ones where each one is capable of learning, reasoning, and collaborating.

That’s the foundation of MCP:

Every SONiC agent becomes an independent node that can:

• Understand its local state
• Exchange context with peers
• Coordinate decisions through the MCP layer

Together, they form a federation of specialized minds which means faster, more resilient, and inherently aware of the whole.

The Multi-Agent Coordination Plane is a distributed intelligence fabric that connects multiple SONiC agents into one unified reasoning system.

In our Agentic AI architecture, MCP acts like a nervous system for the network:

• Each agent (Config, Telemetry, Topology, Security) behaves like a neuron.
• MCP is the synaptic layer that carries signals and aligns actions.
• Together, they create a collective intelligence becoming self-aware, self-optimizing, and contextually driven.

MCP is more of a business enabler than being an architectural improvement.
It turns reactive infrastructure into a self-optimizing system that saves time, cost, and risk.

By distributing reasoning across nodes, MCP transforms the data center from an operational burden into resilient, compliant, and aware ecosystem.

Here’s how collaboration unfolds inside PalC’s Agentic AI framework:

• Intent Interpretation: The orchestrator translates operator intent (e.g., “Deploy a 4-leaf, 1-spine fabric with telemetry enabled”).
• Delegation via MCP: Tasks are distributed into Configuration sets up interfaces, Topology maps links, Telemetry preps sensors.
• State Synchronization: Agents continuously share updates, ensuring decisions remain consistent and validated.
• Adaptive Execution: MCP learns from each event, fine-tuning coordination for future scenarios.

SONiC, through MCP, shifts from being managed to self-managing.

Each agent grows smarter through experience:

• Config Agent: Learns from historical changes to suggest safer rollouts.
• Telemetry Agent: Detects patterns to predict congestion or performance drift.
• Topology Agent: Recalculates paths dynamically under load or failure.
• Security Agent: Applies policies based on live context, not static rules.

Through MCP, these agents share learning, building a network that’s intelligently aware.

MCP turns SONiC fabrics into cooperative, evolving systems

Our MCP framework fuses AI reasoning, SONiC’s openness, and operational discipline into a distributed, resilient control model.

The goal is to give networks the ability to handle complexity, so humans can focus on innovation.

The outcome:

• Networks that heal themselves.
• Operations that think in context.
• Infrastructure that acts with intent.

Networks are knowledge systems. They generate massive amounts of unstructured intelligence like telemetry, syslogs, event traps, policy states and most of which remains underutilized.

RAG converts this operational exhaust into reasoning fuel. It enables AI models to:

• Retrieve live context: What’s happening across fabrics, clusters, and tenants.
• Ground reasoning: Align insights with real-time configurations.
• Generate precision: Produce factual, explainable outcomes.

In networking terms, RAG is the bridge between observability and cognition — it converts visibility into understanding.

RAG’s value lies not only in the workflow, but also in the reasoning feedback that emerges from it.

1. Collect & Curate: SONiC telemetry, NetPro metrics, logs, configs.
2. Index Knowledge: Create a searchable intelligence layer of historical and live data.
3. Retrieve Context: Query relevant slices (“What caused leaf-03 reboot last night?”).
4. Generate Reasoning: AI synthesizes causal narratives or configuration recommendations.
5. Learn & Adapt: Verified responses become part of the retriever’s future context.

This loop makes networks progressively smarter, not just faster.

• Root Cause Reasoning: Move beyond correlation — infer causation with evidence.
• Policy Intelligence: Detect and explain compliance drifts across vendors.
• Cognitive Assistants: Natural-language diagnostics for L1 engineers.
• Contextual Configs: Generate validated SONiC/BGP/EVPN templates grounded in current state.
• Adaptive Learning: Retain lessons from every RCA, ticket, or anomaly.

In effect, RAG creates a knowledge memory for the network which acts as a living library that improves operational trust and speed.

At PalC Networks, our journey through SONiC-based fabrics, AI observability, and cloud-native orchestration has naturally converged toward RAG-driven network cognition.

Our focus areas include:

• Integrating NetPro Suite as a real-time retrieval layer, grounding AI in verified telemetry.
• Domain-tuned AI models that understand network semantics — from L2 loops to RoCEv2 optimizations.
• Cross-vendor contextual reasoning to unify visibility across SONiC, Cisco, Juniper, and Arista environments.

As contributors to the SONiC ecosystem and the Linux Foundation, we’re advancing an open, cognitive networking paradigm — where intelligence is shared, transparent, and self-improving.

Enterprises adopting RAG-based network intelligence typically realize:

• 60% faster RCA through retrieval-grounded context.
• Reduced operational overhead via explainable AI triage.
• Improved onboarding as natural language replaces CLI silos.
• Lower TCO by extending reasoning across multi-vendor networks.

The next generation of networks not only just detect or report; they’ll reason, decide, and adapt.
AI agents will retrieve evidence, simulate outcomes, and execute remediations with policy assurance.

RAG is the cognitive fabric that enables the turning static data into continuous intelligence.
It’s how networks evolve from visibility to comprehension, and from automation to autonomy.