See the original post on Medium: https://substack.com/@cconikee/p-165727043
Where Intelligence Lives
The AI landscape is undergoing a fundamental shift. While everyone’s talking about agents and complex orchestration systems, the real revolution is happening at two critical layers: Group Relative Policy Optimization (GRPO), which pushes intelligence deeper into the neural network during training, and Model Context Protocol (MCP), which provides elegant capability access during inference.
This isn’t just another architectural trend—it’s about where intelligence resides and how it gets activated.
GRPO – Reward-Driven Neural Weight Sculpting
The Traditional RL Problem
Classic reinforcement learning in LLMs treats the entire model as a black box:
Input → [GIANT BLACK BOX] → Output → Reward Signal → Adjust Everything
This is like trying to teach someone to play the piano by randomly adjusting every muscle in their body after each note. GRPO takes a radically different approach.
GRPO: Group-Level Intelligence Emergence
GRPO (Group Relative Policy Optimization) applies reinforcement learning at the sub-network level, creating specialized neural circuits that compete and collaborate:
GRPO Architecture
Input Layer: ╔═══╗ ╔═══╗ ╔═══╗ ╔═══╗ ╔═══╗ ╔═══╗ ╔═══╗ ╔═══╗
║x₁ ║ ║x₂ ║ ║x₃ ║ ║x₄ ║ ║x₅ ║ ║x₆ ║ ║x₇ ║ ║x₈ ║
╚═╤═╝ ╚═╤═╝ ╚═╤═╝ ╚═╤═╝ ╚═╤═╝ ╚═╤═╝ ╚═╤═╝ ╚═╤═╝
│ │ │ │ │ │ │ │
└─────┼─────┼─────┘ │ │ │ │
│ │ │ │ │ │
Group A: ╔═══════▼═════▼═══════╗ │ │ │ │
(Logic) ║ W₁₁ W₁₂ ║───┼─────┼─────┼──── Reward_A: +0.8
║ W₂₁ W₂₂ ║ │ │ │
╚═════════════════════╝ │ │ │
│ │ │
Group B: ╔═════════════════════════▼═════▼═════▼═══╗
(Creativity) ║ W₃₃ W₃₄ W₃₅ W₃₆ ║──── Reward_B: +0.3
║ W₄₃ W₄₄ W₄₅ W₄₆ ║
╚═══════════════════════════════════════╝
│
Output Layer: ╔═════════════════════════▼═══════════════╗
║ Final Response ║
╚═══════════════════════════════════════╝
What’s happening here in simple terms:
- Input comes in: The same prompt (like “Solve this math problem”) goes to all groups
- Groups specialize: Group A focuses on logical reasoning, Group B on creative thinking
- Different processing: Each group processes the input using its specialized weights (W₁₁, W₁₂, etc.)
- Separate rewards: Each group gets scored independently – Group A got 0.8 (good logical answer), Group B got 0.3 (less relevant creative response)
- Competition drives improvement: Groups that perform better get strengthened, weaker ones get reduced attention
This is like having different experts in your brain – a math expert, a creative expert, etc. – all working on the same problem, but the brain learns to trust whichever expert is most relevant for each type of question.
How is this different from a simple neural network?
First, let’s understand how traditional neural networks work:
Forward Propagation (how regular neural networks think):
Input → Layer 1 → Layer 2 → Layer 3 → Output
"2+2" [weights] [weights] [weights] "4"
Data flows forward through layers, each layer processes and passes to the next. Like an assembly line where each station takes one step.
Backpropagation (how regular neural networks learn):
Input → Layer 1 → Layer 2 → Layer 3 → Output → Error
↓
←------ Update ALL weights ←-----------┘
When the network makes a mistake, it calculates the error and updates ALL weights in ALL layers based on that single error signal. Like giving everyone in the factory the same feedback regardless of which station caused the problem.
Reinforcement Learning (RL) adds rewards:
Action → Environment → Reward (+1 for correct, -1 for wrong)
↓ ↓
Network ←-- Update weights based on reward
The network gets rewarded for good actions, punished for bad ones. But again, ALL weights get updated the same way.
Now here’s how GRPO is revolutionarily different:
In a traditional neural network, when you train it, you update ALL weights equally based on the final result:
Simple Neural Network:
Input → [Weight₁] → [Weight₂] → [Weight₃] → Output → Single Reward
↑ ↑ ↑
Update ALL weights by the same amount based on final reward
GRPO works differently – it creates specialized teams within the network:
GRPO Network:
Input → [Group A: Logic Weights] → Reward_A (+0.8)
→ [Group B: Creative Weights] → Reward_B (+0.3)
→ [Group C: Memory Weights] → Reward_C (+0.6)
Key difference: Instead of one global error signal updating everything, GRPO rewards different groups based on how much each group contributed to the solution.
Step-by-step on how GRPO works:
- Divide the network into groups: Instead of one big network, GRPO splits it into specialized groups (like having different departments in a company)
- Each group tackles the same problem differently:
- Group A (Logic): Focuses on step-by-step reasoning
- Group B (Creativity): Thinks outside the box
- Group C (Memory): Recalls similar past problems
- Score each group separately: Rather than one final grade, each group gets its performance score
- Reward the best performers: Groups that help solve the problem get strengthened, and others get weakened
- Next time, winners get more influence: The model automatically pays more attention to groups that proved useful
Real example:
- Question: “What’s 2+2 and explain why?”
- Simple NN: Updates all weights based on whether the final answer was good
- GRPO: Logic group gets high reward for “4”, Explanation group gets high reward for “because of the addition”, Creative group gets low reward (wasn’t needed)
This creates specialized intelligence where different brain parts become experts at different types of thinking, like how humans have different cognitive strengths.
DeepSeek’s Implementation: A Concrete Example
Let’s examine how DeepSeek applies GRPO to its mixture-of-experts architecture:
DeepSeek GRPO Weight Adjustment
Attention Head Groups
╔═══════════════════════════════════════════════════════════════╗
║ ║
║ Group 1: Factual Recall │ Group 2: Reasoning ║
║ │ ║
║ ╔══╗ ╔══╗ ╔══╗ ╔══╗ │ ╔══╗ ╔══╗ ╔══╗ ╔══╗ ║
║ ║H₁║ ║H₂║ ║H₃║ ║H₄║ │ ║H₅║ ║H₆║ ║H₇║ ║H₈║ ║
║ ╚═╤╝ ╚═╤╝ ╚═╤╝ ╚═╤╝ │ ╚═╤╝ ╚═╤╝ ╚═╤╝ ╚═╤╝ ║
║ └────╤────────┘ │ └────╤────────┘ ║
║ │ │ │ ║
║ ╔════▼════╗ │ ╔════▼════╗ ║
║ ║W_factual║ │ ║W_reason ║ ║
║ ╚═════════╝ │ ╚═════════╝ ║
║ │ ║
╚═══════════════════════════════┼═══════════════════════════════╝
│
╔═══════════════════════════════▼═══════════════════════════════╗
║ Routing Network ║
║ ║
║ "What's the capital of France?" ──→ Group 1 (0.9) ║
║ "Solve this logic puzzle" ──→ Group 2 (0.8) ║
║ ║
╚═══════════════════════════════════════════════════════════════╝
│
╔═══════════════════════════════▼═══════════════════════════════╗
║ Reward Calculation ║
║ ║
║ Task: "What's 2+2 and why?" ║
║ Group 1 output: "4" ──→ R₁ = +0.6 ║
║ Group 2 output: "because math" ──→ R₂ = +0.9 ║
║ Combined quality score ──→ R_total = +0.8 ║
║ ║
╚═══════════════════════════════════════════════════════════════╝
Step-by-step breakdown:
- Question arrives: “What’s 2+2 and why?” comes into the system
- Router decides: The routing network sends it to both groups – Group 1 (factual recall) gets weight 0.9, Group 2 (reasoning) gets weight 0.8
- Groups process differently:
- Group 1 (H₁-H₄ attention heads) focuses on retrieving the fact “4”
- Group 2 (H₅-H₈ attention heads) focuses on explaining reasoning “because math.”
- Rewards calculated:
- Group 1 gets R₁ = +0.6 (correct but incomplete)
- Group 2 gets R₂ = +0.9 (adds valuable explanation)
- Combined score: R_total = +0.8
- Learning happens: Group 2’s weights get strengthened more because it scored higher, making the model better at providing explanations in the future.
The key insight: instead of training the entire massive model, GRPO trains different “specialist teams” within the model, making each team better at what they’re good at.
The Weight Update Magic
Here’s where GRPO gets brilliant. Instead of updating all weights uniformly, it adjusts groups based on their relative contribution:
Before GRPO Update After GRPO Update
Group A: ║████████░░║ (0.6) ──→ ║██████████║ (0.8) ← Higher reward
Group B: ║███░░░░░░░║ (0.3) ──→ ║██░░░░░░░░║ (0.2) ← Lower reward
Group C: ║██████░░░░║ (0.5) ──→ ║█████████░║ (0.7) ← Medium reward
Weight Adjustment Formula:
Δw_i = α × (R_i - R_avg) × ∇_w_i × importance_mask_i
Where:
╔═══════════════════════════════════════════════════════════════╗
║ R_i = Reward for group i ║
║ R_avg = Average reward across all groups ║
║ importance_mask_i = Attention weights for group i ║
╚═══════════════════════════════════════════════════════════════╝
Simple steps of what happens:
- Measure performance: Each group gets a score (0.6, 0.3, 0.5 in our example)
- Calculate the average: (0.6 + 0.3 + 0.5) ÷ 3 = 0.47 average
- Find who’s above/below average:
- Group A: 0.6 – 0.47 = +0.13 (above average, gets boosted)
- Group B: 0.3 – 0.47 = -0.17 (below average, gets reduced)
- Group C: 0.5 – 0.47 = +0.03 (slightly above,slightl boost)
- Adjust weights proportionally: Good performers get stronger connections, poor performers get weaker.
- Next tim,: The model automatically pays more attention to the groups that proved helpful
Think of it like a team where high performers get more responsibility and resources, while underperformers get less, but everyone stays on the team and can improve.
The Neural Competition Dynamics
GRPO creates an internal economy within the neural network:
Neural Group Competition
High-Performing Groups Low-Performing Groups
╔═══════════════════╗ ╔═══════════════════╗
║ Stronger ║ ║ Weaker ║
║ Connections ║ ║ Connections ║
║ ║ ║ ║
║ ████████████ ◄────╫──────────╫───► ██░░░░░░░░ ║
║ (More Neurons) ║ Compete ║ (Fewer Neurons) ║
║ ║ ║ ║
║ Gets More ║ ║ Gets Less ║
║ Attention in ║ ║ Attention in ║
║ Future Tasks ║ ║ Future Tasks ║
║ ║ ║ ║
╚═══════════════════╝ ╚═══════════════════╝
│ │
│ │
└────── Reward Signal ───────┘
Drives Selection
How this competition works in practice:
- All groups start equal: Initially, every neural group has a similar influence
- Performance creates winners: Groups that consistently provide good answers get rewarded
- Winners get more resources: Successful groups grow stronger connections (more filled bars ████)
- Losers get fewer resources: Underperforming groups get weaker connections (less filled bars ░░░░)
- Future tasks favor winners: When similar questions come up, the model automatically listens more to proven performers
- Continuous adaptation: This happens continuously – groups can rise and fall based on their current usefulness
Real example: If the model encounters lots of math problems:
- The “mathematical reasoning” group performs well and grows stronger
- The “creative writing” group performs poorly on math and shrinks
- Next time a math question appears, the model automatically routes more attention to the math group
- But if a creative writing task appears later, groups can shift again
This creates a dynamic, self-organizing intelligence where the model rewires itself based on what works.
The Technology Stack Evolution
Current State: Agent-Heavy Architecture
Most AI systems today look like this clunky mess:
Traditional Agent Stack
╔════════════════════════════════════════════════════════════════╗
║ AGENT LAYER ║
║ ║
║ ╔═══════════╗ ╔═══════════╗ ╔═══════════╗ ╔═══════════╗ ║
║ ║ Planning ║ ║ Memory ║ ║ Tool ║ ║ Reasoning ║ ║ ← Brittle
║ ║ Agent ║ ║ Agent ║ ║ Agent ║ ║ Agent ║ ║ Rules
║ ╚═════╤═════╝ ╚═════╤═════╝ ╚═════╤═════╝ ╚═════╤═════╝ ║
║ │ │ │ │ ║
║ └─────────────┼─────────────┼─────────────┘ ║
║ │ │ ║
╚═════════════════════╤═════════════╤══════════════════════════╝
│ │
╔═════════════════════▼═════════════▼══════════════════════════╗
║ LLM CORE ║
║ ║
║ ╔══════════════════════════════════════════════════════════╗ ║
║ ║ Transformer Layers ║ ║ ← Actual
║ ║ Input ──→ Attention ──→ FFN ──→ Output ║ ║ Intelligence
║ ╚══════════════════════════════════════════════════════════╝ ║
║ ║
╚══════════════════════════════════════════════════════════════╝
│
╔═════════════════════▼════════════════════════════════════════╗
║ CAPABILITY LAYER ║
║ ║
║ ╔═══════════╗ ╔═══════════╗ ╔═══════════╗ ╔═══════════╗ ║
║ ║ File I/O ║ ║ Web ║ ║ Database ║ ║ API ║ ║ ← Ad-hoc
║ ║ ║ ║ Scraping ║ ║ Access ║ ║ Calls ║ ║ Interfaces
║ ╚═══════════╝ ╚═══════════╝ ╚═══════════╝ ╚═══════════╝ ║
║ ║
╚══════════════════════════════════════════════════════════════╝
The Problem with Agent Scaffolding
Agent layers are fundamentally rule-based systems masquerading as intelligence:
Agent Decision Tree (Brittle):
if task.type == "search":
route_to_search_agent()
elif task.complexity > threshold:
route_to_planning_agent()
elif task.requires_memory:
route_to_memory_agent()
else:
route_to_default_agent()
# What happens when you encounter:
# "Find me a restaurant that my grandmother would have liked
# based on her cooking style from our last conversation"
#
# → Rules break down, multiple agents conflict, brittleness emergesFuture State: GRPO + MCP Architecture
Here’s where we’re headed (and where smart money should bet):
GRPO + MCP Architecture
╔════════════════════════════════════════════════════════════════╗
║ THIN AGENT LAYER ║
║ ║
║ ╔══════════════════════════════════════════════════════════╗ ║
║ ║ Minimal Orchestration ║ ║ ← Minimal
║ ║ (Just workflow routing) ║ ║ Rules
║ ╚══════════════════════════════════════════════════════════╝ ║
║ ║
╚════════════════════════╤═══════════════════════════════════════╝
│
╔════════════════════════▼═══════════════════════════════════════╗
║ LLM + GRPO CORE ║
║ ║
║ Input: "Book a restaurant my grandma would like" ║
║ │ ║
║ ▼ ║
║ ╔══════════════════════════════════════════════════════════╗ ║
║ ║ GRPO Group Activation ║ ║
║ ║ ║ ║
║ ║ Memory Group: ║█████████║ (0.9) ← High ║ ║ ← Learned
║ ║ Preference Group: ║██████████║(1.0) ← Max ║ ║ Intelligence
║ ║ Location Group: ║███████░░░║(0.7) ← Medium ║ ║
║ ║ Booking Group: ║████████░░║(0.8) ← High ║ ║
║ ╚══════════════════════════════════════════════════════════╝ ║
║ │ ║
║ ▼ ║
║ Generated Plan: "Search Italian restaurants near ║
║ user, filter by cozy atmosphere, book for 2" ║
║ ║
╚════════════════════════╤═══════════════════════════════════════╝
│
╔════════════════════════▼═══════════════════════════════════════╗
║ MCP LAYER ║
║ ║
║ ╔══════════════════════════════════════════════════════════╗ ║
║ ║ Capability Registry ║ ║
║ ║ ║ ║
║ ║ restaurant_search: ║ ║
║ ║ provider: "yelp_mcp" ║ ║ ← Standardized
║ ║ methods: [search, filter, rate] ║ ║ Interface
║ ║ ║ ║
║ ║ booking_service: ║ ║
║ ║ provider: "opentable_mcp" ║ ║
║ ║ methods: [reserve, cancel, modify] ║ ║
║ ╚══════════════════════════════════════════════════════════╝ ║
║ ║
╚════════════════════════════════════════════════════════════════╝
Part 3: MCP vs Traditional Tool Integration
The MCP Advantage
Model Context Protocol provides a standardized, declarative way to expose capabilities:
Traditional Tool Integration vs MCP
Traditional: MCP:
╔═════════════╗ ╔═════════════╗
║ Custom API ║ ║ Standard ║
║ Integration ║ ║ MCP Server ║
║ ║ ║ ║
║ def search( ║ ║ { ║
║ query, ║ ║ "name": ║
║ filters, ║ VS ║ "search", ║
║ auth_key, ║ ║ "params": ║
║ endpoint, ║ ║ {...}, ║
║ headers ║ ║ "schema": ║
║ ): ║ ║ {...} ║
║ # 47 lines║ ║ } ║
║ ... ║ ║ ║
╚═════════════╝ ╚═════════════╝
↑ Brittle ↑ Elegant
↑ Custom ↑ Standard
↑ Breaks ↑ Composable
MCP Protocol Deep Dive
MCP Communication Flow
LLM Core MCP Server
╔════════════════╗ ╔════════════════╗
║ ║ 1. Discovery ║ ║
║ ║ ─────────────► ║ ║
║ ║ ║ Available: ║
║ ║ 2. Capability║ - search ║
║ ║ ◄───────────── ║ - book ║
║ ║ Manifest ║ - review ║
║ ║ ║ ║
║ ║ 3. Invoke ║ ║
║ ║ ─────────────► ║ ║
║ "search ║ search( ║ ╔════════════╗ ║
║ italian ║ query='ital',║ ║ Yelp API ║ ║
║ cozy" ║ filters={ ║ ║ ║ ║
║ ║ 'atmosphere':║ ║ ║ ║
║ ║ 'cozy'}) ║ ╚════════════╝ ║
║ ║ ║ ║
║ ║ 4. Results ║ ║
║ ║ ◄───────────── ║ ║
║ Process & ║ [{ ║ ║
║ Continue ║ "name":"...",║ ║
║ ║ "rating":...,║ ║
║ ║ "location":.}]║ ║
╚════════════════╝ ╚════════════════╝
Why MCP Wins Over Agent Orchestration
Complexity Comparison
Agent Orchestration Complexity:
╔═══════════╗ ╔═══════════╗ ╔═══════════╗
║ Agent A ║───►║ Agent B ║───►║ Agent C ║
║ Plan ║ ║ Execute ║ ║ Verify ║
║ ║ ║ ║ ║ ║
║ if X then ║ ║ try Y ║ ║ check Z ║
║ route ║ ║ catch E ║ ║ if fail ║
║ else if ║ ║ retry N ║ ║ retry ║
║ ... ║ ║ timeout ║ ║ else ║
║ [50 LoC] ║ ║ [80 LoC] ║ ║ [60 LoC] ║
╚═══════════╝ ╚═══════════╝ ╚═══════════╝
↓
Total Complexity: 190+ LoC
Failure Points: N × M × K
MCP + GRPO Complexity:
╔═══════════╗ ╔═══════════╗
║ LLM ║────►║ MCP ║
║ Plan ║ ║ Exec ║
║ Via ║ ║ Via ║
║ GRPO ║ ║ Proto ║
║ [0 LoC] ║ ║ [5 LoC] ║
╚═══════════╝ ╚═══════════╝
↓
Total: 5 LoC
Failure Points: 1
Neural Networks vs Rules – The Fundamental Distinction
Why Neural Networks Crush Rule-Based Systems
Problem: "Find a restaurant for date night"
Rule-Based Agent: Neural Network (GRPO):
╔═══════════════════════╗ ╔═══════════════════════╗
║ if occasion == date: ║ ║ Context Vector: ║
║ ambiance = romantic ║ ║ [0.8, 0.2, 0.9, ║
║ price = mid_high ║ ║ 0.1, 0.7, ...] ║
║ elif occasion == ...: ║ ║ ↓ ║
║ ... ║ VS ║ All Groups Active: ║
║ ║ ║ Romance: 0.9 ║
║ # What about: ║ ║ Budget: 0.6 ║
║ # "casual romantic" ║ ║ Location: 0.8 ║
║ # "budget date" ║ ║ Cuisine: 0.4 ║
║ # "anniversary lunch" ║ ║ → Emergent decision ║
║ # → Rules explode! ║ ║ ║
╚═══════════════════════╝ ╚═══════════════════════╝
The Generalization Power
Neural networks learn patterns and relationships, and rules encode specific cases:
Rule System Coverage vs Neural Coverage
Rule System: Neural Network:
╔═══════════════════════╗ ╔═══════════════════════╗
║ Known Cases: ║ ║ Learned Patterns: ║
║ ╔═╗ ╔═╗ ╔═╗ ╔═╗ ║ ║ ╭─────────────────╮ ║
║ ║A║ ║B║ ║C║ ║D║ ║ ║ │ Continuous │ ║
║ ╚═╝ ╚═╝ ╚═╝ ╚═╝ ║ ║ │ Relationship │ ║
║ ║ VS ║ │ Space │ ║
║ Unknown Case: ║ ║ │ │ ║
║ ╔═╗ ║ ║ │ A B C D │ ║
║ ║?║ ← Breaks ║ ║ │ ● ● ● ● ● ● ● │ ║
║ ╚═╝ ║ ║ │ ↑ │ ║
║ ║ ║ │ Interpolates │ ║
╚═══════════════════════╝ ║ ╰─────────────────╯ ║
╚═══════════════════════╝
The Convergence Thesis
Intelligence is Moving Inward and Downward
My thesis: Intelligence is gradually evolving toward two primary layers, though this will likely be a 3-5 year evolution rather than rapid displacement:
- GRPO-Enhanced LLMs (Training & Inference Intelligence)
- MCP Capability Layer (Deterministic Execution)
The Great Convergence
Current (Fragmented) Future (Consolidated)
╔═══════════════════╗ ╔═══════════════════╗
║ Agents ║ ║ Thin Router ║
║ ╔═╗ ╔═╗ ╔═╗ ╔═╗ ║ ║ │ ║
║ ║A║ ║B║ ║C║ ║D║ ║ ───────► ║ ▼ ║
║ ╚═╝ ╚═╝ ╚═╝ ╚═╝ ║ ║ ╔═════════════╗ ║
╚═══════════════════╝ ║ ║ GRPO ║ ║
╔═══════════════════╗ ║ ║ Enhanced ║ ║
║ LLM ║ ║ ║ LLM ║ ║
║ ╔═════════════╗ ║ ║ ║ ║ ║
║ ║Basic Attn ║ ║ ║ ║ Specialized ║ ║
║ ╚═════════════╝ ║ ║ ║Sub-Networks ║ ║
╚═══════════════════╝ ║ ╚═════════════╝ ║
╔═══════════════════╗ ╚═══════════════════╝
║ Tools/APIs ║ ╔═══════════════════╗
║ ╔═╗ ╔═╗ ╔═╗ ╔═╗ ║ ║ MCP Layer ║
║ ║1║ ║2║ ║3║ ║4║ ║ ║ ╔═════════════╗ ║
║ ╚═╝ ╚═╝ ╚═╝ ╚═╝ ║ ║ ║ Standardized║ ║
╚═══════════════════╝ ║ ║ Capabilities║ ║
║ ╚═════════════╝ ║
╚═══════════════════╝
Why This Evolution Makes Sense
Economic Pressure: Fewer moving parts = less maintenance cost
Technical Pressure: Neural networks outperform rules at scale
User Experience: Seamless intelligence vs fragmented tool switching
However, this transition will likely be gradual rather than disruptive. Current evidence suggests we’re in the early stages of a 3-5 year evolution where both approaches will coexist, with GRPO+MCP gradually gaining ground as the technology matures.
Efficiency Comparison
Agent-Heavy System: GRPO + MCP System:
╔══════════════╗ ╔══════════════╗
║ Token Usage ║ ║ Token Usage ║
║ Agent A: 1000║ ║ GRPO Core: ║
║ Agent B: 800 ║ ║ 2000 ║
║ Agent C: 1200║ ║ ║
║ Coordination:║ VS ║ MCP Calls: ║
║ 500 ║ ║ 50 ║
║ ║ ║ ║
║ Total: 3500 ║ ║ Total: 2050 ║
║ ║ ║ ║
║ Latency: High║ ║ Latency: Low ║
║ Errors: Many ║ ║ Errors: Few ║
╚══════════════╝ ╚══════════════╝
Practical Implications & Betting Strategy
Where to Place Your Chips
If you’re building AI systems today, here’s where to focus:
Investment Priority Matrix
High Impact │
│ ╔═══════════╗
│ ║ GRPO ║
│ ║ Training ║
│ ╚═══════════╝
│ ╔═══════════╗
Medium │ ║ MCP ║
Impact │ ║ Protocol ║
│ ╚═══════════╝
│╔═══════════╗
Low Impact ║║ Agent ║
║║ Scaffold ║
║║ ║
╚╚═══════════╝═══════════════════
Low Medium High
Complexity
Implementation Roadmap
Phase 1: Embrace MCP Now (0-12 months)
- Replace custom tool integrations with MCP servers
- Standardize capability interfaces
- Build reusable MCP components
Phase 2: Experiment with GRPO (6-18 months)
- Implement group-based reward systems
- Test sub-network specialization
- Measure performance vs traditional RL
Phase 3: Gradually Reduce Agent Complexity (12-36 months)
- Simplify orchestration layers where proven effective
- Push decision-making into the LLM for appropriate use cases
- Use agents primarily for workflow routing and coordination
The evolution will likely be incremental rather than revolutionary—organizations should prepare for both approaches to coexist while gradually shifting toward the more efficient GRPO+MCP stack as it matures.
The Technical Bet
Technology Evolution Curve
Capability
▲
│ ╔════════ GRPO + MCP (Future)
│ ╱
│ ╱
│ ╱ ╔════════ Current Agent Systems
│ ╱ ╱
│ ╱ ╱
│ ╱ ╱
│╱ ╱
╫ ╱
│ ╱
│╱
└──────────────────────────────────────────────►
Time
Technical Debt and Complexity
▲
│ ╔════════ Agent Scaffolding
│ ╱
│ ╱
│ ╱
│ ╱
│ ╱ ╔════════ GRPO + MCP
│╱ ╱
╫ ╱
│ ╱
│ ╱
│ ╱
│╱
└──────────────────────────────────────────────►
Time
Conclusion: The Intelligence Evolution
The future of AI isn’t in complex agent orchestration systems with brittle rules. It’s in intelligent neural networks that can dynamically allocate their internal resources through GRPO, combined with elegant capability protocols like MCP that provide deterministic access to tools and services.
We’re witnessing the early stages of a fundamental shift from external coordination (agents managing other agents) to internal optimization (neural groups competing and collaborating within a single model). Combined with standardized capability protocols, this creates a much more robust, efficient, and maintainable architecture.
However, this evolution will likely be gradual rather than disruptive. Current evidence suggests both approaches will coexist for the next 3-5 years, with GRPO+MCP gaining ground as the technology matures and proven use cases emerge.
The smart money isn’t betting on more complex agent frameworks. It’s betting on deeper intelligence in the neural networks, paired with cleaner interfaces to the outside world.
The evolution is inevitable. The question is: will you be ahead of it or behind it?
Final thought: Just as the internet evolved from complex, proprietary protocols to simple, standard ones (HTTP/TCP), AI architecture is evolving from complex, custom agent systems to simple, intelligent neural networks with standard capability protocols. GRPO + MCP isn’t just a technical choice—it’s betting on the natural evolution of complex systems toward elegant simplicity.