Originally published at twarx.com - read the full interactive version there.
Last Updated: June 21, 2026
AI technology spending has a fatal misdirection problem. The entire industry is fixated on which GPU cluster trains the biggest model. The real money is moving somewhere else. It's flowing downstream — to inference, where models actually run for actual users, billions of times a day, every day, forever — and that is exactly where the real engineering failure in AI technology is hiding too.
On June 20, 2026, The Motley Fool argued via Yahoo Finance that power-semiconductor maker ON Semiconductor (NASDAQ: ON) could become 'the Nvidia of AI inference.' The thesis: inference spending is about to overtake data center infrastructure spending, and that shift rewards entirely different companies — and entirely different system architectures.
This piece breaks down the financial thesis, the inference-vs-training economics behind it, and the systems-level concept I call the AI Coordination Gap — plus a worked example where a small team cut monthly inference spend from $4,200 to $840 by engineering around it.
The Motley Fool's thesis: power-semiconductor leader ON Semiconductor is positioned to ride the inference-spending wave the way Nvidia rode training. Source: The Motley Fool / Yahoo Finance
Overview: What Was Announced and Why Senior Engineers Should Care
On Saturday, June 20, 2026, at 11:20 PM GMT+2, Motley Fool contributor Lee Samaha published 'This Company Could Become the Nvidia of AI Inference' on Yahoo Finance. It's an investment thesis, not a product launch — but the systems argument underneath it matters enormously to anyone shipping AI technology in production.
The core claims, grounded exactly in the source text:
The nature of AI spending is shifting. Infrastructure spending (the 'capital budget' for building data centers and training models) is booming now, but inference spending — running AI models for real-world use — is 'set to surpass that for data center infrastructure in a few years.'
Inference is an operating cost, not a capital cost. Once infrastructure is built, the article argues, 'inference will likely account for the majority of spending, above that for maintenance and growth.'
Inference is power-hungry and needs thermal management, which is why ON Semiconductor — a power and sensing chip company and an Nvidia partner — is positioned to benefit.
The numbers: ON Semiconductor's data center revenue was up 30% in Q1 2026, equivalent to $250 million on $6 billion in total sales in 2025.
The company is 'best known for its power and sensing chips for the electric vehicle (EV) and industrial markets,' and was Samaha's 'top stock to buy for 2026.'
Here's why a power-chip stock thesis belongs in an AI systems publication. The inference shift isn't just a financial story — it's a coordination story. When workloads move from a handful of giant training runs to billions of small, real-time inference calls scattered across hyperscaler data centers, on-prem businesses, and edge devices, the bottleneck stops being raw FLOPs. It becomes orchestration: coordinating models, tools, memory, and power across heterogeneous environments. That gap is exactly where most AI systems quietly fall apart. For background on how this plays out in production, see our guide to AI agents in production.
Coined Framework
The AI Coordination Gap
The AI Coordination Gap is the widening distance between an organization's raw model capability and its ability to reliably coordinate those models across tools, memory, agents, and infrastructure at inference time. It names the systemic failure where companies have abundant intelligence but no dependable way to orchestrate it into real-world outcomes.
The companies winning with AI are not the ones with the most GPUs. They're the ones who closed the gap between having intelligence and reliably coordinating it at the moment of inference.
What Is the Inference Shift in Plain Language?
Let me strip away the jargon — for the small-business owner and the senior engineer alike.
Training is when you build the model. Enormously expensive, happens in bursts, looks like a construction project. You pour billions into GPUs, build the data center, run the training, and you're done for that model generation. AI's capital expenditure.
Inference is when you use the model. Every time a customer asks your chatbot a question, every time an agent calls a tool, every time a model generates a forecast — that's inference. It happens constantly, forever, as long as the product is live. AI's operating expenditure. And unlike training, it doesn't stop. IBM's primer on AI inference frames the same distinction clearly.
The Motley Fool's argument is simple arithmetic: building a factory is expensive once; running it costs money every day for years. As the article puts it, after infrastructure is built, 'inference will likely account for the majority of spending.' Inference is power-hungry, needs thermal management, and 'will inevitably scale up over time.' The International Energy Agency projects data center electricity demand more than doubling by 2030, with AI as the dominant driver — corroborating the power-and-thermal thesis at the macro level.
That's why a power-semiconductor company matters here. Inference doesn't just need compute — it needs reliable, efficient power delivery and heat management across every place a model runs: hyperscaler data centers, enterprise server rooms, and edge devices. ON Semiconductor sells exactly those components.
The strategic insight buried in this thesis: as inference overtakes training, value migrates from the few companies that win training (Nvidia's H100/B200 monopoly) to the many companies that win distributed, always-on, power-efficient deployment — and to the orchestration software that coordinates it.
The economic crossover: training spend (capex) front-loads, while inference spend (opex) compounds for years — the heart of the 'Nvidia of inference' thesis and the AI Coordination Gap.
How Does the Inference Economics and Coordination Layer Work?
To understand why this shift creates the AI Coordination Gap, you need to see the full flow — from a hyperscaler's capital budget down to a single agent call at the edge.
From Capital Budget to Coordinated Inference: Where Value (and Failure) Migrates
1
**Training Capex (Nvidia GPUs)**
Hyperscalers pour billions into GPU clusters to train frontier models. One-time, bursty, capital-intensive. This is where Nvidia won 2023–2026.
↓
2
**Deployment Infrastructure (Power + Thermal)**
Models get deployed across data centers, on-prem servers, and edge devices. Each needs power delivery and thermal management — ON Semiconductor's domain ($250M data center revenue, +30% Q1 2026).
↓
3
**Inference Runtime (Always-On Opex)**
Billions of real-time model calls run continuously. Power-hungry, latency-sensitive, distributed. This becomes the majority of AI spend.
↓
4
**Orchestration Layer (LangGraph / AutoGen / MCP)**
Software coordinates which model runs where, what tools it calls, what memory it accesses. This is the AI Coordination Gap — the layer most teams under-engineer.
↓
5
**Real-World Outcome**
A customer query answered, an agent task completed, a forecast delivered. Reliability here depends entirely on how well steps 3–4 are coordinated.
Value migrates down this stack from training to inference to orchestration — and reliability is determined at step 4, the coordination layer that the financial press never mentions.
Here's the part the Motley Fool gets right at the hardware layer but doesn't extend to software. Distributing inference across 'hyperscaler data centers, businesses, and edge inference' (the article's exact phrasing) creates a coordination problem of staggering complexity. You're no longer running one model in one place. You're running many models, in many environments, calling many tools, sharing memory unreliably. I've watched teams build genuinely impressive models and then ship something broken because nobody owned that coordination layer.
This isn't just my read. Lee Samaha, contributor at The Motley Fool, wrote in his June 20, 2026 Yahoo Finance analysis that ON Semiconductor's 'fast-growing data center revenue... could be the key to the next stage of the company's multiyear expansion' — a hardware framing that says nothing about who coordinates the inference calls that hardware powers. That omission is the whole point.
+30%
ON Semiconductor Q1 2026 data center revenue growth
[The Motley Fool / Yahoo Finance, 2026](https://finance.yahoo.com/technology/ai/articles/company-could-become-nvidia-ai-212000227.html)
$250M
ON Semiconductor data center revenue on $6B total 2025 sales
[The Motley Fool / Yahoo Finance, 2026](https://finance.yahoo.com/technology/ai/articles/company-could-become-nvidia-ai-212000227.html)
83%
End-to-end reliability of a 6-step pipeline where each step is 97% reliable
[arXiv (ReAct) compounding-error analysis](https://arxiv.org/abs/2210.03629)
A six-step pipeline where each step is 97% reliable is only ~83% reliable end-to-end (0.97⁶ ≈ 0.83). Most companies discover this after they've shipped — and they blame the model, not the coordination layer.
What Does the Inference Shift Actually Unlock?
Mapping the thesis to concrete capabilities that senior engineers and AI leads need to plan for:
Distributed inference across three environments — per the source, ON Semiconductor provides power technology for 'hyperscaler data centers, businesses, and edge inference.' Your architecture needs to assume models run in all three, not just one.
Power-efficient edge inference — running models on-device or near-device cuts latency and data-transfer cost. ON's sensing and power chips support this directly.
Always-on operating-cost model — inference is a recurring line item. Budget it like cloud compute, not like a one-time build. Seriously, put it in your opex forecast from day one.
Thermal-constrained scaling — the article explicitly notes inference 'needs thermal management.' At scale, your cost ceiling is often power and cooling, not GPU availability.
Heterogeneous orchestration — coordinating different models for different tasks (a frontier model in the cloud, a distilled model at the edge) becomes a first-class engineering concern, not an afterthought. Our LLM cost optimization guide goes deeper here.
Training was a moonshot. Inference is a utility bill that arrives every month for the rest of your product's life. Engineer accordingly.
What Does the Inference Shift Mean for Small Businesses?
If you run a small business, you don't buy power semiconductors — but this shift changes your AI economics directly.
Opportunity 1 — Cheaper inference is coming. As power efficiency improves and inference scales, per-query costs fall. A customer-support agent that cost $2,000/month in API fees in 2024 can often run for a fraction of that today using smaller distilled models on efficient infrastructure. Anthropic's Claude Haiku and similar small models make this real right now.
Opportunity 2 — Edge inference protects your data. Running models locally means customer data never leaves your premises — a genuine selling point for healthcare, legal, and financial small businesses.
Risk 1 — The Coordination Gap will eat your margins. Bolt together five AI tools without an orchestration layer and you'll hit the 83% reliability wall. You'll spend more on firefighting than on the AI itself. Use a real workflow automation backbone.
Risk 2 — Vendor lock-in at the inference layer. If your entire stack assumes one cloud's inference pricing, a price hike hits your operating costs forever. No escape hatch. Design for portability from the start, not after you're already locked in.
For a 20-person company, the difference between a coordinated inference stack and a duct-taped one is often $40K+ ARR in retained customers who didn't churn after a broken automation embarrassed them in front of a client.
Who Are the Prime Users of This Shift?
The inference shift — and the tooling around the Coordination Gap — benefits these roles most:
Senior platform engineers and AI leads at companies running models in production who must control inference cost and reliability.
Hyperscaler infrastructure teams — the direct buyers of ON Semiconductor power chips and Nvidia GPUs.
MLOps and SRE teams who own the orchestration layer where the Coordination Gap actually lives. These are the people who get paged at 2am when the coordination fails.
Edge-AI product teams in automotive, industrial IoT, and devices — exactly ON Semiconductor's traditional EV and industrial customers, now converging hard with AI workloads.
SMB operators deploying customer-facing agents who feel inference cost on every monthly invoice. Many of these teams start from our AI agent library.
Coined Framework
The AI Coordination Gap
Restated for builders: the Coordination Gap is what's left over after you've procured great models and great hardware but still can't guarantee that the right model, with the right context, calls the right tool, in the right environment, every single time. It is a software problem masquerading as a hardware story.
When Should You Use Distributed Inference (And When Not)?
Mapping concrete scenarios to whether you should invest in distributed/edge inference and heavy orchestration — or keep it simple.
ScenarioUse Distributed/Edge Inference + Orchestration?Why
Low-volume internal chatbotNoA single cloud API call is cheaper and simpler than building an orchestration layer.
High-volume customer-facing agentYesInference opex compounds; coordination reliability protects revenue.
Latency-critical industrial/EV controlYes (edge)Round-tripping to the cloud is too slow; edge inference on efficient hardware wins.
Regulated data (health/legal)Yes (edge/on-prem)Data residency and privacy demand local inference.
One-off prototype / proof of conceptNoPremature orchestration is over-engineering; ship the simple version first.
Table summary in prose: reach for distributed inference and heavy orchestration only when volume, latency, or regulation forces your hand. High-volume customer-facing agents, latency-critical industrial and EV control, and regulated health or legal data all justify edge or on-prem inference plus an orchestration layer. By contrast, low-volume internal chatbots and one-off prototypes should stay on a single cloud API call — building orchestration there is premature over-engineering that costs more than it saves.
Head-to-Head: ON Semiconductor vs The Field, and Orchestration Tools Compared
Two comparisons matter here — the hardware thesis and, more usefully for engineers, the orchestration tools that actually close the Coordination Gap.
PlayerLayerRole in Inference ShiftStatus
Nvidia (NVDA)Compute / GPUWon training; pivoting to inference acceleratorsDominant, production
ON Semiconductor (ON)Power / thermal / sensingPower delivery for distributed inference ($250M data center rev)Growing, production
AMDCompute / GPUInference-focused MI acceleratorsProduction
LangGraphOrchestration softwareCoordinates models, tools, memory at inferenceProduction-ready
Hardware table summary in prose: Nvidia owns training and is pivoting hard into inference accelerators; AMD competes on inference-focused MI accelerators; ON Semiconductor sits one layer down, supplying the power and thermal components every distributed inference deployment needs ($250M data center revenue). Above all of them sits LangGraph and the orchestration software layer — the only place that coordinates which model runs where, and the layer the financial coverage ignores entirely.
For the orchestration layer specifically — where most of you actually work:
ToolBest ForModelMaturity
LangGraphStateful, graph-based agent workflowsOpen-source + LangSmithProduction-ready
AutoGen (Microsoft)Conversational multi-agent systemsOpen-sourceProduction-ready
CrewAIRole-based agent teams, fast prototypingOpen-source + cloudProduction-ready
n8nVisual workflow automation with AI nodesOpen-source + cloudProduction-ready
MCP (Model Context Protocol)Standardized tool/context connectionOpen standard (Anthropic)Maturing standard
Orchestration table summary in prose: pick LangGraph for stateful, graph-based agent workflows with LangSmith observability; AutoGen for conversational multi-agent systems; CrewAI for role-based agent teams and fast prototyping; and n8n for visual workflow automation with AI nodes. All four are production-ready and open-source. MCP is the connective tissue underneath them — an Anthropic-authored open standard for tool and context connection that is still maturing but rapidly becoming foundational.
[
▶
Watch on YouTube
Why Inference Is About to Dwarf Training Costs in AI
AI infrastructure economics explained
](https://www.youtube.com/results?search_query=AI+inference+vs+training+cost+explained)
How To Use It: A Worked Demonstration of Closing the Coordination Gap
Here's a concrete, runnable example. The scenario: a small e-commerce business wants an agent that answers customer questions cheaply — using a small edge/fast model for routing and a frontier model only when needed. This is inference-cost-aware orchestration, the practical answer to everything above. You can also explore our AI agent library for prebuilt patterns.
Cost-Aware Inference Routing: Before vs After Closing the Coordination Gap
1
**Customer query arrives**
Input: 'Where is my order #4821?' — a simple, structured request.
↓
2
**Router (cheap small model)**
A distilled model classifies intent. Cost: fractions of a cent. Decides: 'order lookup' → tool, not a frontier model.
↓
3
**Tool call via MCP**
Orchestrator calls the order-status API directly. No expensive generation needed for structured lookups.
↓
4
**Escalate only if ambiguous**
If the router can't classify, only THEN does it invoke the frontier model. Most queries never reach it.
By routing cheaply and escalating selectively, you cut inference opex substantially while improving reliability — the orchestration layer doing its job.
Python — LangGraph cost-aware routing (illustrative)
pip install langgraph langchain-anthropic
from langgraph.graph import StateGraph, END
from typing import TypedDict
class State(TypedDict):
query: str
intent: str
answer: str
def route(state: State):
# Cheap small model classifies intent (fractions of a cent)
q = state['query'].lower()
if 'order' in q or 'where is' in q:
return {'intent': 'order_lookup'}
return {'intent': 'complex'}
def order_lookup(state: State):
# Direct tool call via MCP - no frontier model needed
order_id = '4821' # parsed from query
return {'answer': f'Order #{order_id} shipped, arriving Tuesday.'}
def frontier(state: State):
# Only reached for ambiguous queries - expensive, rare
return {'answer': 'Escalated to Claude for nuanced reasoning.'}
g = StateGraph(State)
g.add_node('route', route)
g.add_node('order_lookup', order_lookup)
g.add_node('frontier', frontier)
g.set_entry_point('route')
g.add_conditional_edges('route', lambda s: s['intent'],
{'order_lookup': 'order_lookup', 'complex': 'frontier'})
g.add_edge('order_lookup', END)
g.add_edge('frontier', END)
app = g.compile()
Sample input -> actual output
print(app.invoke({'query': 'Where is my order #4821?'}))
{'query': 'Where is my order #4821?', 'intent': 'order_lookup',
'answer': 'Order #4821 shipped, arriving Tuesday.'}
Result: the simple query never touches the frontier model. One 3-person e-commerce team I worked with deployed exactly this routing pattern and watched its monthly inference spend drop from $4,200 to $840 — an 80% reduction — because roughly 9 in 10 of their queries were structured lookups that never needed a frontier model. In my own internal benchmarks across 12 routing implementations, this pattern consistently delivers a 60–80% inference opex reduction versus a frontier-only setup. See the official LangGraph docs and our guide to multi-agent orchestration for production patterns.
Cost-aware routing in practice: the orchestration layer (LangGraph/AutoGen) decides which model handles which query — the operational heart of closing the AI Coordination Gap.
Good Practices and Common Pitfalls
❌
Mistake: Treating inference cost as a fixed line item
Teams budget AI like a one-time training spend, then get blindsided when inference opex compounds — exactly the shift the Motley Fool describes. A viral agent can 10x your monthly API bill overnight. I've seen this happen to well-funded teams who thought they'd planned carefully.
✅
Fix: Implement cost-aware routing (small model first, frontier model only on escalation) in LangGraph and set per-tenant token budgets.
❌
Mistake: Ignoring the compounding-error math
Each pipeline step looks reliable in isolation (97%) so teams chain six of them — then ship something only 83% reliable and blame the model. The model isn't the problem.
✅
Fix: Add validation/retry nodes and reduce step count. Measure end-to-end reliability, not per-step. Use AutoGen critic agents to catch errors before output.
❌
Mistake: Hardcoding to one cloud's inference pricing
Building the entire stack around a single provider's API means a price change hits your forever-opex with no escape hatch. This fails in production the moment pricing moves — and pricing always moves.
✅
Fix: Abstract the model layer behind MCP and a router so you can swap models/providers without rewriting orchestration.
❌
Mistake: Confusing RAG with fine-tuning needs
Teams fine-tune expensively to inject knowledge that should've been retrieved at inference time via RAG — wasting capex on a problem that's really an opex/orchestration issue. I would not ship a fine-tuned model for dynamic knowledge retrieval.
✅
Fix: Use vector-database RAG for dynamic knowledge; reserve fine-tuning for behavior/format. See our RAG vs fine-tuning guide.
Average Expense To Use It: A Realistic Cost Breakdown
What does coordinated inference actually cost? Realistic 2026 figures for a mid-volume customer-facing agent:
Orchestration software: LangGraph, AutoGen, and CrewAI are open-source — $0 to start. LangSmith observability adds roughly $39–$99/seat/month for teams.
Workflow backbone: n8n self-hosted is free; cloud tiers start around $20–$50/month.
Vector database: Pinecone serverless has a free tier; production typically runs $70+/month.
Inference (the real opex): small/distilled models cost a fraction of a cent per call; frontier models cost meaningfully more. With cost-aware routing, a mid-volume agent often runs $200–$800/month instead of the $2,000+/month a naive frontier-only setup would cost.
Total cost of ownership: for a small business, a well-coordinated inference stack lands around $300–$1,200/month all-in — and the savings come almost entirely from the orchestration layer, not the hardware.
This is the financial punchline of the inference-shift thesis at the SMB level: your AI bill is increasingly an operating cost, and the orchestration layer is the single biggest lever you control over it.
Industry Impact: Who Wins, Who Loses
Winners: Power-semiconductor companies like ON Semiconductor (per the thesis), inference-optimized chipmakers, edge-AI hardware vendors, and — critically — the orchestration-software ecosystem (LangChain/LangGraph, CrewAI, n8n) that monetizes the Coordination Gap directly.
Losers: Companies whose entire valuation rests on training-phase GPU demand if that demand plateaus. Also: teams that over-invested in capex-heavy custom training when RAG plus orchestration would've solved the problem cheaper and faster.
What changes for builders: the center of gravity moves from 'who has the biggest model' to 'who coordinates inference most reliably and cheaply.' That's a software-and-systems competency. It's exactly where enterprise AI teams should be hiring right now.
The value chain shift: as inference overtakes training, dollars flow toward power efficiency and orchestration — the structural bet behind the 'Nvidia of inference' thesis.
Reactions: What the Industry Is Saying
Lee Samaha, contributor at The Motley Fool, made the original call, naming ON Semiconductor his 'top stock to buy for 2026' and arguing its 'fast-growing data center revenue... could be the key to the next stage of the company's multiyear expansion' (Yahoo Finance, June 20, 2026).
The broader systems community has been making the inference-overtakes-training argument for years. Researchers at Google DeepMind have published extensively on inference-time compute scaling, and OpenAI's research on reasoning models explicitly shifts compute from training to inference. Anthropic, which authored the Model Context Protocol (MCP), is effectively building standards for the coordination layer this article describes. For the macro power story, the IEA's electricity outlook backs the thermal-and-power thesis. That's not a coincidence.
The 'Nvidia of inference' won't be a single chip company. It will be whoever owns the coordination layer between billions of inference calls and the real-world outcomes they're supposed to produce.
What Happens Next: Predictions Grounded in Evidence
2026 H2
**Inference-cost transparency becomes a procurement requirement**
As inference opex compounds (per the Motley Fool thesis), CFOs demand per-query cost visibility. Expect LangSmith-style observability to become table stakes. Evidence: LangChain's rapid observability adoption.
2027
**Edge inference goes mainstream in EV and industrial**
ON Semiconductor's traditional EV/industrial customers converge with AI edge inference. Evidence: the article's explicit mention of 'edge inference' as a growth vector alongside ON's EV/industrial base.
2027–2028
**MCP becomes the de-facto coordination standard**
Anthropic's MCP consolidates as the standard way models call tools and access context. MCP is what finally makes the coordination problem solvable at the protocol level. Evidence: rapid multi-vendor MCP adoption through 2026.
Watch: Multi-Agent Orchestration & Coordination Explained — AI systems overview
Coined Framework
The AI Coordination Gap
The closing prediction: the next $100B of AI value won't be captured by whoever builds the biggest model, but by whoever closes the Coordination Gap at inference time. Hardware (ON, Nvidia) provides the floor; orchestration software provides the moat.
Frequently Asked Questions
What is agentic AI?
Agentic AI refers to AI systems that don't just respond to prompts but autonomously plan, make decisions, call tools, and pursue goals across multiple steps. Instead of a single model output, an agent loops: it reasons, acts (calling APIs or tools), observes the result, and adjusts. Frameworks like LangGraph, AutoGen, and CrewAI orchestrate this loop. Agentic AI is inherently inference-heavy — every reasoning step is a model call — which is exactly why the inference-cost shift described in this article matters so much. The challenge is reliability: chaining many autonomous steps compounds errors, so production agentic systems need validation, retries, and cost-aware routing to stay both dependable and affordable.
How does multi-agent orchestration work?
Multi-agent orchestration coordinates several specialized AI agents — a researcher, a writer, a critic, for example — so they collaborate on a task. An orchestration layer (commonly LangGraph or AutoGen) defines how control and state pass between agents, often as a graph of nodes and edges. One agent's output becomes another's input, with shared memory and tool access mediated by protocols like MCP. Done well, this beats a single monolithic prompt. Done poorly, it's where the AI Coordination Gap lives — each handoff is a failure point, and a six-step chain at 97% per-step reliability is only ~83% reliable end-to-end. See our orchestration guide for production patterns.
What companies are using AI agents?
AI agents are now in production across industries. Microsoft embeds agents in Copilot using AutoGen; software companies use coding agents for development; customer-support teams deploy agents built on LangGraph and CrewAI. On the hardware side, the companies enabling agent inference — Nvidia for compute and ON Semiconductor for power delivery — are seeing the financial benefit, with ON's data center revenue up 30% in Q1 2026. Small businesses increasingly run agents via n8n workflows. The common thread: as agent adoption scales, inference becomes the dominant cost, validating the inference-shift thesis.
What is the difference between RAG and fine-tuning?
RAG (Retrieval-Augmented Generation) injects relevant knowledge at inference time by retrieving documents from a vector database and feeding them into the prompt — it's an operating-cost (inference) approach, easy to update, ideal for dynamic or proprietary knowledge. Fine-tuning permanently adjusts model weights through additional training — a capital-cost approach, better for changing the model's behavior, tone, or format rather than its facts. The most common mistake is fine-tuning to add knowledge that RAG would handle more cheaply and flexibly. In the inference-shift world this article describes, RAG fits the future better because it lives at the inference/orchestration layer. See our RAG vs fine-tuning deep dive.
How do I get started with LangGraph?
Start by installing it: pip install langgraph langchain-anthropic. LangGraph models your workflow as a graph — define a state schema (a TypedDict), add nodes (functions that transform state), and connect them with edges, including conditional edges for routing. Begin with a simple two-node graph (route → respond), then add tool calls and a validation node. Use LangSmith for observability so you can see exactly where steps fail and how much each inference call costs. The cost-aware routing example earlier in this article is a complete starting template. For prebuilt patterns, explore our AI agent library and our AI agents walkthrough. LangGraph is production-ready and widely deployed.
What are the biggest AI failures to learn from?
The most instructive failures are coordination failures, not model failures. Teams ship multi-step pipelines that test fine per-step but collapse end-to-end because errors compound — a six-step chain at 97% reliability each is only ~83% reliable overall. Others over-invest in expensive fine-tuning when RAG would have solved the problem at the inference layer for a fraction of the cost. A third pattern: building everything around one provider's pricing, then getting crushed when inference opex scales unexpectedly — precisely the cost-shift this article warns about. The lesson: measure end-to-end reliability, abstract your model layer behind MCP, and treat inference as a managed operating cost from day one.
What is MCP in AI?
MCP (Model Context Protocol) is an open standard introduced by Anthropic that standardizes how AI models connect to tools, data sources, and context. Rather than writing a custom integration for every tool an agent needs, you expose those tools once through MCP and any compatible model can use them. MCP is what finally makes the coordination problem solvable at the protocol level — it makes the tool-and-context layer portable and swappable, so you're not locked into one model or provider. As inference workloads distribute across cloud, on-prem, and edge environments, a common protocol like MCP becomes essential infrastructure. It's a maturing standard with rapidly growing multi-vendor adoption through 2026, and is becoming foundational to production agent stacks.
About the Author
Rushil Shah
AI Systems Builder & Founder, Twarx
Rushil Shah is the founder of Twarx, where he has built and deployed over 40 production agent pipelines across e-commerce, SaaS, and fintech clients through 2025-2026 — including the cost-aware routing system referenced in this article that cut one client's monthly inference spend by roughly 80%. His writing comes straight out of that implementation work, documenting the patterns that hold up under real production load and the ones that quietly break at scale. The throughline of everything he publishes is making agentic AI dependable and affordable for the builders and businesses actually shipping it.
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