Agentic MCP

Agentic MCP is the next evolution of our Model Coordination Protocol—designed for systems where models alone aren’t enough, and intelligent agents must decide how models are used, when, and why.

It blends the reliability of MCP with the reasoning ability of agents, transforming static routing into dynamic, context-aware orchestration led by a distributed network of AI personas.

What It Adds

The original MCP handled:

Model fallback
Endpoint routing
Execution retries
Schema validation

Agentic MCP builds on this by introducing:

Autonomous agents that act as routers, critics, retriers, or supervisors
Reasoning loops around execution decisions
Chain governance, where agents vote, escalate, or revise coordination plans
Contextual overrides based on memory, goals, or project-specific preferences

Core Architecture

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

Each stage is modular and role-driven. Agents can be retrained, swapped, or specialized.

Agent Roles

Planner: Breaks tasks into subgoals or tool calls
Model Selector: Chooses the optimal model based on metrics, past performance, or user preference
Executor: Runs the model call or chain
Critic: Reviews outputs, flags issues
Validator: Enforces schema, safety, or logic rules
Finalizer: Rewrites, compresses, or structures the final output

Every message between agents uses our A2A Protocol, maintaining traceability and structure.

Runtime Behavior

Agents run in parallel when possible (e.g. multiple selectors can compete)
Logs are stored per-agent, per-decision
Memory systems allow agents to learn from coordination history
Results include a full reasoning graph, not just a flat output

Example Use Case

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

Steps:

Planner splits: [Fetch specs] + [Summarize papers] + [Generate pros/cons]
Selector routes to GPT-4 for paper summary, Claude for tone, internal DB for specs
Critic spots hallucinated data, reroutes for verification
Finalizer assembles into bullet list + markdown output

How It's Powered

Serverless endpoints run each agent in parallel or in-chain
Cluster memory volumes store intermediate traces and memories
TensorOne Eval hooks benchmark coordination outcomes
MCP remains the backend, but is now agent-led instead of hard-coded

Research Themes

Meta-reasoning: Can agents reflect on the routing decisions themselves?
Agent negotiation: Disagreeing models, conflicting opinions, escalation logic
Cost-aware routing: Performance × cost × latency scoring
Personality-driven coordination: Let agents “argue” based on style, tone, bias

Why It Matters

Model orchestration is no longer just about API calls and fallbacks.

It’s about coordinating intelligence—letting agents make the call, challenge each other, and ensure quality at every step.

Agentic MCP is not just infrastructure.
It’s infrastructure with intent.