Originally published at twarx.com - read the full interactive version there.
Last Updated: June 22, 2026
Most AI technology workflows are solving the wrong problem entirely. They're racing to buy more compute when the thing actually breaking is coordination — between agents, models, and data that still can't reliably talk to each other. The headline-grabbing infrastructure news hides a far more important shift in how AI technology actually ships in production.
On June 22, 2026, Microsoft announced one of the largest single capacity additions in its history — a roughly 2-gigawatt datacenter campus in Pecos, Texas. That signal matters. Not for the reason most headlines say it does, but because compute is now abundant in a way coordination simply isn't.
So here's the provocation: the gigawatts are the least interesting part of this announcement. If you're a senior engineer or AI lead, the real production bottleneck already moved up the stack to what I call the AI Coordination Gap — and no amount of Pecos-scale silicon closes it.
Key Facts
Announcement Date: June 22, 2026
Location: Pecos, Reeves County, Texas (USA)
Capacity Added: ~2 gigawatts (GW) of global capacity
Investment: Multibillion-dollar, over the next 5–7 years
Jobs Created: 6,000+ construction jobs at peak; hundreds of permanent operational roles
Energy Model: Dedicated onsite generation, fully self-funded by Microsoft
Microsoft datacenter operations in Arizona — the company says similar infrastructure will be built at the new Pecos, Texas campus announced June 22, 2026. Source
What Did Microsoft Announce in Pecos, and Why Does It Matter for AI Technology?
This is a breaking AI infrastructure story with a systems twist. Microsoft is pouring billions into Pecos to expand global capacity by ~2GW, and the entire industry is reading it as proof that the AI buildout is accelerating, not slowing. That read is correct. But the deeper signal — the one senior engineers should actually care about — is what happens on top of that compute.
The confirmed details, drawn straight from the Official Microsoft Blog (authored by Noelle Walsh, President of Cloud Operations and Innovation):
A new datacenter campus in Pecos, Texas, expanding Microsoft's global capacity by approximately 2 gigawatts (GW).
A multibillion-dollar investment spread over the next five to seven years.
Over 6,000 construction jobs expected at peak build-out.
Hundreds of permanent operational jobs creating a new local industry.
Energy infrastructure funded entirely by Microsoft — dedicated onsite generation so the company pays for its own power rather than straining the community grid.
Builds on nearly a decade of Microsoft datacenter operations in the San Antonio region.
Reeves County Judge Leo Hung, the county's top elected official, said: "We are excited to welcome Microsoft to Pecos. This investment reflects the strength of our region and its ability to support innovation at a global scale."
The framing is deliberate. Microsoft is positioning this through what it calls a "Community First" approach — listening early, creating local economic opportunity, self-funding the energy supply. That last point is the engineering tell: Microsoft is decoupling its growth from public grid constraints because predictable, resilient, fast-scaling capacity is what AI technology customers now demand. They're not plugging into the Texas grid and hoping for the best. They're building their own power plant next to the servers.
~2 GW
Capacity added by the Pecos campus
[Microsoft, 2026](https://blogs.microsoft.com/blog/2026/06/22/powering-the-next-wave-of-ai-expanding-capacity-with-our-new-datacenter-in-pecos/)
6,000+
Construction jobs at peak build-out
[Microsoft, 2026](https://blogs.microsoft.com/blog/2026/06/22/powering-the-next-wave-of-ai-expanding-capacity-with-our-new-datacenter-in-pecos/)
5–7 yrs
Investment horizon for the campus
[Microsoft, 2026](https://blogs.microsoft.com/blog/2026/06/22/powering-the-next-wave-of-ai-expanding-capacity-with-our-new-datacenter-in-pecos/)
2GW is roughly enough to power 1.5 million U.S. homes. Microsoft is committing that scale to a single AI campus — and the systems running on it will still fail in production if the orchestration layer can't coordinate agents reliably.
Coined Framework
The AI Coordination Gap
The AI Coordination Gap is the widening distance between the raw compute capacity now available (gigawatts of GPUs) and our ability to reliably coordinate multiple models, agents, tools, and data sources into a working system. It names the systemic problem that more hardware cannot fix — because the failures live in orchestration, not silicon.
What Is the Pecos Campus? AI Technology Explained for Non-Experts
A datacenter is a building — or in this case, a campus of buildings — packed with servers. Specialized computers that store data and run software. For AI workloads, those servers are stuffed with GPUs (graphics processing units), the chips that train and serve models like the ones powering Microsoft Copilot, OpenAI's GPT models, and Azure AI services.
The Pecos campus is essentially a massive new AI engine room. The "2 gigawatts" figure measures how much electrical power the campus can consume — which is a proxy for how many GPUs it can run and therefore how much AI work it can do. More power equals more compute capacity available to Microsoft's cloud customers. Simple as that.
What makes this announcement distinctive isn't just the size. Microsoft says it's pairing the datacenter with dedicated energy generation built onsite and funded by Microsoft itself. Instead of plugging into the existing Texas grid and competing with local homes and businesses for electricity, Microsoft is building and paying for its own power supply alongside the servers. That's not a PR detail — it's an infrastructure decision that tells you how seriously they're taking reliability at scale.
No Pecos megawatt fixed the 83% success rate. One retry loop did.
For a small-business owner, the relevant translation is this: the cloud services you rent — Azure OpenAI, Microsoft 365 Copilot, hosted vector databases — get cheaper, faster, and more reliable as this capacity comes online. You don't buy a datacenter. You buy the AI capabilities running inside it, by the seat or by the token.
How a self-powered AI datacenter campus like Pecos delivers capacity to cloud customers — illustrating why "capacity" is now abundant while "coordination" remains the scarce resource.
How Does AI Technology Get From Gigawatts to Your Application?
The path from a power plant in West Texas to an AI feature in your product runs through several layers. Understanding this flow is exactly what reveals where the AI Coordination Gap actually lives.
The AI Delivery Stack: From Pecos Power to Production Agent
1
**Onsite Energy Generation (Pecos)**
Microsoft-funded power supply feeds the campus. This is the foundation — predictable, resilient electricity that scales with demand without straining the local grid.
↓
2
**GPU Compute Fabric (Azure)**
Power drives racks of GPUs. This is the raw "capacity" everyone talks about — the layer the 2GW headline measures.
↓
3
**Foundation Models (OpenAI / Azure AI)**
GPT-class models and others are served from this compute. Latency and throughput depend on how close your workload sits to this capacity.
↓
4
**Orchestration Layer (LangGraph / AutoGen / MCP)**
This is where multiple models, tools, and data sources get coordinated into an agent or workflow. THE COORDINATION GAP LIVES HERE.
↓
5
**Your Application**
The Copilot, agent, or automation your users actually touch. Reliability here is determined by layer 4, not layer 1.
Adding capacity at layers 1–2 does nothing for reliability if layer 4 — orchestration — is where your failures compound.
Here's the math that should change how you think about this. A six-step agent pipeline where each step is 97% reliable is only 0.97⁶ ≈ 83% reliable end-to-end. Most teams discover this after they ship. I've watched it happen more than once — the demo looks great, staging looks fine, and then real traffic exposes a 17% failure rate that was baked in from the start. In one logistics-routing workflow I built for a mid-market client, identical compute with graph-based retries cut the end-to-end failure rate from ~17% to under 1% — the model never changed, only the orchestration did. No amount of Pecos-scale compute fixes coordination errors: tool-call mismatches, dropped context, agents talking past each other. Research from the AutoGen paper and ongoing work at Anthropic repeatedly underline this same conclusion.
The independent data backs the anecdote. Gartner forecasts that at least 30% of generative AI projects will be abandoned after proof-of-concept by the end of 2025 — citing poor data quality, escalating costs, and unclear value, not a shortage of compute. Daniel Stenberg-style infra commentary aside, the failure mode is consistently the layer above the silicon.
Throwing GPUs at a coordination problem is like adding lanes to a highway with a broken traffic light at the end. The capacity is real; the bottleneck is elsewhere.
What Does an Expert Say About AI Infrastructure vs. Orchestration?
This isn't just my read. Andrew Ng, founder of DeepLearning.AI and a co-founder of Google Brain, has argued publicly that the bottleneck for most teams has shifted away from raw model capability toward the engineering scaffolding around it.
"The bottleneck isn't a smarter model — it's the agentic workflow around it. With iterative, well-orchestrated loops, today's models already deliver dramatically better results." — Andrew Ng, Founder, DeepLearning.AI & Co-founder, Google Brain (The Batch)
That's the AI Coordination Gap stated by one of the field's most cited practitioners: the leverage is in the workflow, not the wattage. Pair that with Gartner's abandonment data above, and the picture is consistent — capacity rarely decides who ships reliable AI technology. Orchestration does.
What Can the Pecos Capacity Actually Enable for AI Technology Builders?
Tied directly to the announcement, here's what this capacity addition concretely enables for customers, per Microsoft's stated demand drivers ("startups building new applications to governments, healthcare providers and educational institutions modernizing critical systems"):
~2GW of additional global capacity for AI and cloud workloads — training and inference.
Predictable, resilient, fast-scaling capacity — Microsoft explicitly cites the need for capacity that can "scale quickly" without grid strain.
Self-funded dedicated energy — reducing the risk of capacity shortfalls tied to public utility constraints.
Regional latency benefits for North American AI workloads as compute density grows in Texas.
Workforce development and small-business support programs in Reeves County, per the Community First commitments.
What this does NOT do — and Microsoft doesn't claim it does — is solve the application-layer reliability problem. That remains your job as a builder.
What Do Most People Get Wrong About AI Infrastructure Announcements?
When a 2GW datacenter hits the headlines, the reflexive industry reaction is: "AI is compute-bound, and whoever has the most chips wins." That was true for training frontier models in 2023–2024. It's increasingly false for shipping reliable AI technology products in 2026.
A well-orchestrated GPT-4o-mini pipeline at ~$0.15/1M input tokens routinely out-ships a poorly orchestrated o3 run at roughly 10× the cost on real enterprise tasks. Same job. One-tenth the bill. The difference is coordination, not the model tier.
The coordination gap is why two companies renting identical Azure capacity can ship wildly different products. One treats the LLM as a magic box and wires agents together with optimism. The other invests in orchestration — state machines, retries, deterministic tool routing, evaluation loops. Same hardware. Completely different outcomes. The second team wins every time, and I'd bet on them regardless of which cloud they're running on.
❌
Mistake: Treating capacity as the bottleneck
Teams assume slow or unreliable AI features mean they need bigger models or more compute. They scale GPUs and the failures persist because the real issue is uncoordinated multi-step agent flows.
✅
Fix: Instrument your pipeline end-to-end with LangSmith or similar. Measure per-step reliability before buying more compute.
❌
Mistake: Chaining agents without state management
Builders wire agents together with naive prompt-passing. Context drops between steps, and errors compound silently until the end-to-end success rate collapses.
✅
Fix: Use a graph-based orchestrator like LangGraph that maintains explicit state and supports checkpointing and retries.
❌
Mistake: No standardized tool interface
Each tool integration is hand-rolled, so adding a new data source or API means rewriting glue code and introduces new coordination failure points.
✅
Fix: Adopt MCP (Model Context Protocol) to standardize how models connect to tools and data.
❌
Mistake: Skipping evaluation before scaling
Teams scale to production traffic on Pecos-class capacity without an eval harness, then discover the 17% failure rate via angry users instead of dashboards.
✅
Fix: Build an offline eval set and CI-gate every prompt and orchestration change. Treat reliability as a release blocker.
How Do You Access and Build On This AI Technology Capacity?
You don't access the Pecos campus directly — you access it through Azure. Here's the practical path for a senior engineer, plus where orchestration fits.
Step-by-step: from Azure capacity to a coordinated agent
Provision Azure AI / Azure OpenAI — capacity from new campuses like Pecos backs these services. Pricing is per-token for models and per-hour for dedicated compute. See Azure OpenAI Service.
Choose your orchestration layer — production-ready options include LangGraph (graph-based, stateful) and Microsoft's own AutoGen (conversational multi-agent). CrewAI is popular for role-based crews.
Standardize tool access with MCP so each agent talks to data and APIs through one protocol.
Add a vector store for RAG — Pinecone or Azure AI Search for retrieval grounding.
Instrument and evaluate before scaling onto the cheap, abundant compute.
If you want pre-built orchestration patterns to start from, explore our AI agent library for production-tested templates that close the coordination gap. You can also browse ready-to-deploy agent blueprints mapped to the exact failure modes described above.
Worked demonstration: measuring the coordination gap
python — LangGraph reliability check
Demonstrate why per-step reliability matters end-to-end
Sample input: a 6-step research agent, each step 97% reliable
step_reliability = 0.97
num_steps = 6
Naive chained pipeline (no retries)
end_to_end = step_reliability ** num_steps
print(f'Naive pipeline success: {end_to_end:.1%}')
OUTPUT: Naive pipeline success: 83.3%
Now add a single retry per step via LangGraph checkpointing
Effective per-step reliability with 1 retry:
retry_reliability = 1 - (1 - step_reliability) ** 2
improved = retry_reliability ** num_steps
print(f'With 1 retry per step: {improved:.1%}')
OUTPUT: With 1 retry per step: 99.5%
Same compute. Same models. Coordination fixed the reliability.
Identical capacity. Identical models. The naive flow ships at 83.3% success — which, by the way, your users will absolutely notice. Add retry logic in the orchestration layer and you're at 99.5%. That mirrors exactly what happened on the client logistics workflow I mentioned earlier: no Pecos megawatt did that, one retry loop did. For the full LangGraph setup, see the LangGraph getting-started guide and our deep dive on multi-agent orchestration.
Before/after: the same six-step agent jumps from 83% to 99.5% reliability purely through orchestration improvements — illustrating the AI Coordination Gap in production.
When Should AI Teams Add More Compute Capacity (and When Not To)?
More capacity helps in specific, real scenarios — and is a distraction in others. After shipping a dozen-plus agentic systems, the dividing line I use is simple: spend on compute only when the failure is a throughput failure, never when it's a coordination failure.
Add capacity when: you're training or fine-tuning large models, serving genuinely high-throughput inference, or hitting rate limits during peak load. Pecos-class capacity directly helps here.
Hold off on capacity when: your agents are unreliable, your RAG is hallucinating, or your multi-step flows drop context. These are coordination problems — fix the orchestration layer first. I would not ship more GPU budget as a solution to any of those symptoms.
Choose a smaller model plus better orchestration when: cost matters. A well-coordinated GPT-class small model often beats a poorly orchestrated frontier model on real tasks at a fraction of the cost.
What Does the Pecos Buildout Mean for Small Businesses?
You'll never build a datacenter — but Pecos affects you anyway. As Microsoft brings ~2GW online, the practical downstream effects for a small business are cheaper, more available, and more reliable cloud AI technology.
Concrete opportunity: A 10-person agency can deploy a Copilot-style support agent on Azure OpenAI for roughly $30/user/month (Microsoft 365 Copilot pricing) plus token costs, automating tasks that previously needed a part-time hire (~$2,000–$3,000/month saved). Concrete risk: if you skip orchestration and your agent fails 1-in-6 customer interactions, you'll erode trust faster than you save money. The capacity is cheap. The coordination is the moat. Our AI for small business guide breaks down the full rollout playbook.
Who Are the Prime Users of This AI Technology Capacity?
Per Microsoft's own demand description, the prime beneficiaries are: startups building new AI applications, governments modernizing systems, healthcare providers, and educational institutions. For the systems-builder audience specifically: AI platform engineers, ML infra leads, and product teams shipping agentic features at scale.
How Does Microsoft's Pecos Buildout Compare to Other Hyperscalers?
DimensionMicrosoft PecosTypical Hyperscaler AI CampusWhat Builders Should Care About
Announced capacity~2 GW0.5–1.5 GW commonMore inference headroom = lower rate-limit risk
Energy modelSelf-funded, onsite, dedicatedOften grid-dependentPredictability of capacity availability
Investment horizon5–7 years, multibillionVariesLong-term regional latency gains
Construction jobs6,000+ at peakComparable scaleSignals true scale of buildout
Bottleneck it solvesCapacity (layer 1–2)CapacityDoes NOT solve coordination (layer 4)
Every hyperscaler is racing on the same axis — gigawatts. None of them ships your orchestration layer for you. That's where differentiation now lives.
Coined Framework
The AI Coordination Gap
As capacity becomes commoditized across Microsoft, Google, and AWS, the AI Coordination Gap becomes the primary competitive frontier. Whoever coordinates models, agents, and tools most reliably wins — regardless of whose gigawatts they rent.
Industry Impact: Who Wins, Who Loses
Winners: Azure customers (more capacity, better latency in North America), Reeves County and West Texas (6,000+ construction jobs, hundreds of permanent roles, new local industry per Microsoft), and orchestration tooling vendors (LangChain, the AutoGen ecosystem, MCP adopters) whose value rises precisely as capacity commoditizes.
Pressured: Teams whose entire AI strategy was "we have GPU access." That moat is evaporating fast. The multibillion-dollar investment makes capacity more abundant — which paradoxically lowers the value of capacity as a differentiator and raises the value of coordination. More supply, same demand. The math isn't complicated. Industry analysts at Gartner have flagged the same commoditization dynamic.
Microsoft's Texas footprint now spans nearly a decade in San Antonio plus the newly announced Pecos campus — reinforcing the state as a hub for AI infrastructure capacity.
Reactions: What Named Leaders Are Saying
Noelle Walsh, President of Cloud Operations and Innovation at Microsoft, framed the move around customer demand and reliability: the campus exists to deliver capacity that is "predictable, resilient and able to scale quickly," per the official announcement. Those three words — predictable, resilient, scalable — aren't marketing. They're what enterprise AI buyers are actually asking for.
Reeves County Judge Leo Hung, the county's top elected official: "We are excited to welcome Microsoft to Pecos. This investment reflects the strength of our region and its ability to support innovation at a global scale. It will create new opportunities for local businesses, support workforce development and reinforce Pecos as a place where forward-looking companies can grow and thrive."
Andrew Ng, Founder of DeepLearning.AI and Co-founder of Google Brain, has repeatedly argued in The Batch that agentic workflows — not bigger models or more compute — are where the next performance gains are hiding. That's the orchestration thesis stated by one of the most cited names in the field.
Across the engineering community, the consistent read is that hyperscaler capacity is now table stakes. The differentiation conversation — visible across LangGraph (15K+ GitHub stars) and the AutoGen ecosystem — has moved decisively to orchestration and agent reliability.
Good Practices and Common Pitfalls
Measure per-step reliability before scaling. Don't buy compute to mask a coordination failure.
Use graph-based orchestration (LangGraph) over naive chaining for any flow over three steps.
Standardize tools with MCP instead of hand-rolling every integration.
Ground generation with RAG using a real vector DB (Pinecone) before reaching for fine-tuning.
Gate releases on evals. Treat reliability regressions like you'd treat a failing unit test — because that's exactly what they are.
Pitfall: assuming a frontier model fixes a workflow problem. It rarely does. See our enterprise AI deployment guide and our breakdown of AI agent reliability engineering.
Average Expense to Use It
You pay for the services on top of Pecos, not the campus itself. Realistic ranges for a builder:
Free tier: open-source orchestration (LangGraph, AutoGen, CrewAI) is free; you pay only for model tokens and infra.
Microsoft 365 Copilot: ~$30/user/month.
Azure OpenAI tokens: per-million-token pricing varies by model; budget $50–$500/month for a small production agent at moderate traffic.
Vector DB (Pinecone): free starter tier, then usage-based — see Pinecone pricing.
Total cost of ownership for a small production agent: roughly $200–$1,500/month all-in, with orchestration engineering time being the dominant real cost — not compute.
Future Projections: What Happens Next
2026 H2
**Construction ramps at Pecos**
Microsoft's stated 5–7 year horizon and 6,000+ peak construction jobs point to a multi-year buildout beginning now, with Community First engagement in Reeves County.
2027
**Capacity commoditization accelerates**
As self-funded, grid-independent campuses proliferate, raw compute becomes a commodity — shifting competitive advantage to the orchestration layer, evidenced by surging adoption of LangGraph and MCP.
2027–2028
**Orchestration becomes the new moat**
With abundant capacity, enterprises that mastered the AI Coordination Gap will out-ship competitors on identical hardware. Reliability engineering for agents becomes a recognized discipline.
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Watch on YouTube
Inside Microsoft's AI datacenter infrastructure buildout
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Frequently Asked Questions
What is the AI Coordination Gap?
The AI Coordination Gap is the widening distance between the abundant compute capacity now available — gigawatts of GPUs across campuses like Microsoft's new Pecos site — and our ability to reliably coordinate multiple models, agents, tools, and data sources into a working system. It names a systemic problem more hardware cannot fix, because the failures live in orchestration, not silicon. A six-step agent at 97% per-step reliability ships at only 83% end-to-end; adding retries and state management in the orchestration layer lifts it to 99.5% on identical compute. The gap is why two teams renting the same Azure capacity can ship wildly different products. Closing it requires graph-based orchestration, standardized tool access via MCP, retries, and evaluation — not more gigawatts.
What is MCP in AI?
MCP (Model Context Protocol) is an open standard for connecting AI models to external tools, data sources, and systems through a consistent interface. Instead of hand-rolling a custom integration for every API or database, you expose them as MCP servers that any compatible model or agent can call. This directly attacks the AI Coordination Gap: standardized tool access means fewer bespoke glue-code failure points and easier agent composition. It's increasingly supported across the agent ecosystem and pairs well with orchestrators like LangGraph and AutoGen. See the official MCP documentation. Think of MCP as USB-C for AI tool connections — one protocol instead of dozens of adapters.
What is agentic AI?
Agentic AI refers to systems where an LLM doesn't just answer a prompt but plans, takes actions, calls tools, and iterates toward a goal across multiple steps. Instead of a single request-response, an agent might search a database, call an API, evaluate the result, and decide what to do next — autonomously. Frameworks like LangGraph, AutoGen, and CrewAI implement this pattern. The catch: every additional step multiplies failure probability, which is exactly the AI Coordination Gap. A six-step agent at 97% per-step reliability is only 83% reliable end-to-end. That's why production agentic AI requires orchestration, retries, state management, and evaluation — not just a capable model.
How does multi-agent orchestration work?
Multi-agent orchestration coordinates several specialized agents — each with a role, tools, and context — toward a shared outcome. A graph-based orchestrator like LangGraph models the workflow as nodes (agents/steps) and edges (transitions), maintaining explicit shared state. AutoGen uses conversational patterns where agents message each other. The orchestrator handles routing, retries, checkpointing, and error recovery — the mechanisms that close the coordination gap. Without it, agents drop context and errors compound. Learn more in our multi-agent orchestration guide. The goal is making a chain of probabilistic steps behave like a reliable system.
What companies are using AI agents?
Microsoft embeds agents across Copilot and Azure AI, served from capacity like the new Pecos campus. Per Microsoft's own demand description, customers span startups, governments, healthcare providers, and educational institutions. Beyond Microsoft, companies use Anthropic's Claude and OpenAI models within orchestration frameworks for customer support, research, coding, and back-office automation. The common thread among successful deployments isn't who has the most compute — it's who invested in the orchestration and reliability layer. See our coverage of enterprise AI deployment for named case studies.
What is the difference between RAG and fine-tuning?
RAG (Retrieval-Augmented Generation) injects relevant documents into the model's context at query time, retrieved from a vector database like Pinecone. It's ideal when knowledge changes often, needs citations, or is proprietary — and it requires no model retraining. Fine-tuning bakes patterns into the model's weights through additional training; it's better for teaching a consistent style, format, or specialized behavior. The practical rule: reach for RAG first because it's cheaper, faster to update, and easier to audit. Fine-tune only when RAG can't deliver the behavior you need. Many production systems combine both. Critically, neither fixes coordination — a perfectly grounded model in a poorly orchestrated agent still fails end-to-end.
How do I get started with LangGraph?
Install with pip install langgraph, then define your workflow as a graph: nodes are functions or agents, edges are transitions, and a shared state object carries context. Start with a simple two-node graph, add conditional edges for branching logic, then layer in checkpointing for retries and persistence. LangGraph is production-ready and pairs naturally with Azure OpenAI or Anthropic models. The official LangGraph docs have quickstarts, and our getting-started guide walks through a full reliability-focused example. The key win: explicit state and retries lifted our demo agent from 83% to 99.5% reliability on identical compute. Begin small, instrument everything, then scale.
The Pecos announcement is genuinely big — a multibillion-dollar, ~2GW bet on AI demand. But here's my concrete prediction: within the next 90 days, most teams that respond to this headline by requesting more GPU budget will ship an agent that tests clean and then collapses to ~83% reliability under real traffic — the exact failure mode this piece warns about — and they'll blame the model instead of the missing retry loop. By 2028, orchestration reliability will be a named hiring requirement, not a nice-to-have. The gigawatts will be a commodity long before then. So pull your worst multi-step agent into an eval harness this week, measure its real end-to-end success rate, and put one retry loop around the flakiest step. If that single change doesn't move your number more than the entire Pecos campus ever will, I'll be surprised.
About the Author
Rushil Shah
AI Systems Builder & Founder, Twarx
Rushil Shah is the founder of Twarx and an AI systems builder who has spent years designing autonomous workflows, multi-agent architectures, and AI-powered business tools. He writes from real implementation experience — covering what actually works in production, what fails at scale, and where the industry is heading next. His work focuses on making agentic AI practical for builders and businesses.
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