Agentic MCP

The next development of the Model Coordination Protocol (MCP), known as agentic MCP, is intended for settings in which orchestration is driven by intelligent agents rather than merely models. It converts static routing into dynamic, context-aware decision-making, allowing distributed AI systems to make real-time decisions about model selection, execution strategies, and output validation.

From MCP to Agentic MCP

The original MCP provided:

• Model fallback
• Endpoint routing
• Execution retries
• Schema and output validation

Agentic MCP builds on this foundation by introducing agent roles that reason, debate, and adapt:

• Agents act as routers, critics, validators, or planners
• Each decision involves reasoning cycles, escalation logic, or goal-based overrides
• Coordination is traceable, modular, and memory-aware

System Architecture

[User Input]
   ↓
[Coordinator Agent]
   ↓
[Planner Agent] → [Model Selector Agent] → [Executor Agent]
   ↓                        ↘
[Critic Agent] ← [Validator Agent]
   ↓
[Finalizer Agent] → Response

Each role operates independently or in concert, using the A2A Protocol for structured communication. Logs, reasoning states, and decisions are preserved throughout the process.

Agent Roles

• Planner: Breaks the task into subtasks, goals, or tool calls
• Model Selector: Chooses the best model based on performance, cost, or historical metrics
• Executor: Runs the model or chain
• Critic: Reviews output, checks coherence or factuality
• Validator: Enforces schema compliance, policy rules, or safety constraints
• Finalizer: Refines and formats the final output for delivery

Agents can be retrained, customized per project, or deployed dynamically based on request complexity.

Runtime Behavior

• Agents can operate in parallel or chained sequences
• Logs are preserved per agent, per decision
• Memory systems track prior outcomes, improving future routing
• Results include a reasoning graph, not just the final response

Example Workflow

Prompt: "Generate a comparison between open-source TTS models."

Execution:

1. Planner breaks the task into:

   • Fetch specs
   • Summarize academic papers
   • Generate pros/cons list

2. Selector Agent:

   • GPT-4 for summarization
   • Claude for tone
   • Internal DB for specifications

3. Critic Agent detects hallucinations, requests rerun for verification

4. Finalizer formats result into markdown with bullet points

Technical Foundation

• Each agent is deployed via TensorOne Serverless Endpoints
• Cluster memory volumes store intermediate state and long-term context
• TensorOne Evals provide performance benchmarking and fallback analysis
• The core MCP engine remains the backend, now agent-controlled

Research Focus Areas

• Meta-reasoning: Agents evaluating their own or others' routing decisions
• Negotiation protocols: Resolving disagreements between agent outputs
• Cost-aware orchestration: Dynamic trade-offs between latency, quality, and cost
• Persona-driven execution: Agents with distinct styles, preferences, or biases

Why It Matters

Static routing is no longer sufficient in multi-model, multi-agent systems.

Agentic MCP brings intent, adaptability, and auditability to model coordination - allowing agents to not only execute but reason about how execution should happen.