Data centers rely heavily on water for cooling, but alternative sources can reduce strain on municipal systems and support sustainability.

The facility is slated to come online in the first quarter of 2026.

Chinese artificial intelligence startup DeepSeek released two powerful new AI models on Sunday that the company claims match or exceed the capabilities of OpenAI's GPT-5 and Google's Gemini-3.0-Pro — a development that could reshape the competitive landscape between American tech giants and their Chinese challengers.

The Hangzhou-based company launched DeepSeek-V3.2, designed as an everyday reasoning assistant, alongside DeepSeek-V3.2-Speciale, a high-powered variant that achieved gold-medal performance in four elite international competitions: the 2025 International Mathematical Olympiad, the International Olympiad in Informatics, the ICPC World Finals, and the China Mathematical Olympiad.

The release carries profound implications for American technology leadership. DeepSeek has once again demonstrated that it can produce frontier AI systems despite U.S. export controls that restrict China's access to advanced Nvidia chips — and it has done so while making its models freely available under an open-source MIT license.

"People thought DeepSeek gave a one-time breakthrough but we came back much bigger," wrote Chen Fang, who identified himself as a contributor to the project, on X (formerly Twitter). The release drew swift reactions online, with one user declaring: "Rest in peace, ChatGPT."

How DeepSeek's sparse attention breakthrough slashes computing costs

At the heart of the new release lies DeepSeek Sparse Attention, or DSA — a novel architectural innovation that dramatically reduces the computational burden of running AI models on long documents and complex tasks.

Traditional AI attention mechanisms, the core technology allowing language models to understand context, scale poorly as input length increases. Processing a document twice as long typically requires four times the computation. DeepSeek's approach breaks this constraint using what the company calls a "lightning indexer" that identifies only the most relevant portions of context for each query, ignoring the rest.

According to DeepSeek's technical report, DSA reduces inference costs by roughly half compared to previous models when processing long sequences. The architecture "substantially reduces computational complexity while preserving model performance," the report states.

Processing 128,000 tokens — roughly equivalent to a 300-page book — now costs approximately $0.70 per million tokens for decoding, compared to $2.40 for the previous V3.1-Terminus model. That represents a 70% reduction in inference costs.

The 685-billion-parameter models support context windows of 128,000 tokens, making them suitable for analyzing lengthy documents, codebases, and research papers. DeepSeek's technical report notes that independent evaluations on long-context benchmarks show V3.2 performing on par with or better than its predecessor "despite incorporating a sparse attention mechanism."

The benchmark results that put DeepSeek in the same league as GPT-5

DeepSeek's claims of parity with America's leading AI systems rest on extensive testing across mathematics, coding, and reasoning tasks — and the numbers are striking.

On AIME 2025, a prestigious American mathematics competition, DeepSeek-V3.2-Speciale achieved a 96.0% pass rate, compared to 94.6% for GPT-5-High and 95.0% for Gemini-3.0-Pro. On the Harvard-MIT Mathematics Tournament, the Speciale variant scored 99.2%, surpassing Gemini's 97.5%.

The standard V3.2 model, optimized for everyday use, scored 93.1% on AIME and 92.5% on HMMT — marginally below frontier models but achieved with substantially fewer computational resources.

Most striking are the competition results. DeepSeek-V3.2-Speciale scored 35 out of 42 points on the 2025 International Mathematical Olympiad, earning gold-medal status. At the International Olympiad in Informatics, it scored 492 out of 600 points — also gold, ranking 10th overall. The model solved 10 of 12 problems at the ICPC World Finals, placing second.

These results came without internet access or tools during testing. DeepSeek's report states that "testing strictly adheres to the contest's time and attempt limits."

On coding benchmarks, DeepSeek-V3.2 resolved 73.1% of real-world software bugs on SWE-Verified, competitive with GPT-5-High at 74.9%. On Terminal Bench 2.0, measuring complex coding workflows, DeepSeek scored 46.4%—well above GPT-5-High's 35.2%.

The company acknowledges limitations. "Token efficiency remains a challenge," the technical report states, noting that DeepSeek "typically requires longer generation trajectories" to match Gemini-3.0-Pro's output quality.

Why teaching AI to think while using tools changes everything

Beyond raw reasoning, DeepSeek-V3.2 introduces "thinking in tool-use" — the ability to reason through problems while simultaneously executing code, searching the web, and manipulating files.

Previous AI models faced a frustrating limitation: each time they called an external tool, they lost their train of thought and had to restart reasoning from scratch. DeepSeek's architecture preserves the reasoning trace across multiple tool calls, enabling fluid multi-step problem solving.

To train this capability, the company built a massive synthetic data pipeline generating over 1,800 distinct task environments and 85,000 complex instructions. These included challenges like multi-day trip planning with budget constraints, software bug fixes across eight programming languages, and web-based research requiring dozens of searches.

The technical report describes one example: planning a three-day trip from Hangzhou with constraints on hotel prices, restaurant ratings, and attraction costs that vary based on accommodation choices. Such tasks are "hard to solve but easy to verify," making them ideal for training AI agents.

DeepSeek employed real-world tools during training — actual web search APIs, coding environments, and Jupyter notebooks — while generating synthetic prompts to ensure diversity. The result is a model that generalizes to unseen tools and environments, a critical capability for real-world deployment.

DeepSeek's open-source gambit could upend the AI industry's business model

Unlike OpenAI and Anthropic, which guard their most powerful models as proprietary assets, DeepSeek has released both V3.2 and V3.2-Speciale under the MIT license — one of the most permissive open-source frameworks available.

Any developer, researcher, or company can download, modify, and deploy the 685-billion-parameter models without restriction. Full model weights, training code, and documentation are available on Hugging Face, the leading platform for AI model sharing.

The strategic implications are significant. By making frontier-capable models freely available, DeepSeek undermines competitors charging premium API prices. The Hugging Face model card notes that DeepSeek has provided Python scripts and test cases "demonstrating how to encode messages in OpenAI-compatible format" — making migration from competing services straightforward.

For enterprise customers, the value proposition is compelling: frontier performance at dramatically lower cost, with deployment flexibility. But data residency concerns and regulatory uncertainty may limit adoption in sensitive applications — particularly given DeepSeek's Chinese origins.

Regulatory walls are rising against DeepSeek in Europe and America

DeepSeek's global expansion faces mounting resistance. In June, Berlin's data protection commissioner Meike Kamp declared that DeepSeek's transfer of German user data to China is "unlawful" under EU rules, asking Apple and Google to consider blocking the app.

The German authority expressed concern that "Chinese authorities have extensive access rights to personal data within the sphere of influence of Chinese companies." Italy ordered DeepSeek to block its app in February. U.S. lawmakers have moved to ban the service from government devices, citing national security concerns.

Questions also persist about U.S. export controls designed to limit China's AI capabilities. In August, DeepSeek hinted that China would soon have "next generation" domestically built chips to support its models. The company indicated its systems work with Chinese-made chips from Huawei and Cambricon without additional setup.

DeepSeek's original V3 model was reportedly trained on roughly 2,000 older Nvidia H800 chips — hardware since restricted for China export. The company has not disclosed what powered V3.2 training, but its continued advancement suggests export controls alone cannot halt Chinese AI progress.

What DeepSeek's release means for the future of AI competition

The release arrives at a pivotal moment. After years of massive investment, some analysts question whether an AI bubble is forming. DeepSeek's ability to match American frontier models at a fraction of the cost challenges assumptions that AI leadership requires enormous capital expenditure.

The company's technical report reveals that post-training investment now exceeds 10% of pre-training costs — a substantial allocation credited for reasoning improvements. But DeepSeek acknowledges gaps: "The breadth of world knowledge in DeepSeek-V3.2 still lags behind leading proprietary models," the report states. The company plans to address this by scaling pre-training compute.

DeepSeek-V3.2-Speciale remains available through a temporary API until December 15, when its capabilities will merge into the standard release. The Speciale variant is designed exclusively for deep reasoning and does not support tool calling — a limitation the standard model addresses.

For now, the AI race between the United States and China has entered a new phase. DeepSeek's release demonstrates that open-source models can achieve frontier performance, that efficiency innovations can slash costs dramatically, and that the most powerful AI systems may soon be freely available to anyone with an internet connection.

As one commenter on X observed: "Deepseek just casually breaking those historic benchmarks set by Gemini is bonkers."

The question is no longer whether Chinese AI can compete with Silicon Valley. It's whether American companies can maintain their lead when their Chinese rival gives comparable technology away for free.

When Liquid AI, a startup founded by MIT computer scientists back in 2023, introduced its Liquid Foundation Models series 2 (LFM2) in July 2025, the pitch was straightforward: deliver the fastest on-device foundation models on the market using the new "liquid" architecture, with training and inference efficiency that made small models a serious alternative to cloud-only large language models (LLMs) such as OpenAI's GPT series and Google's Gemini.

The initial release shipped dense checkpoints at 350M, 700M, and 1.2B parameters, a hybrid architecture heavily weighted toward gated short convolutions, and benchmark numbers that placed LFM2 ahead of similarly sized competitors like Qwen3, Llama 3.2, and Gemma 3 on both quality and CPU throughput. The message to enterprises was clear: real-time, privacy-preserving AI on phones, laptops, and vehicles no longer required sacrificing capability for latency.

In the months since that launch, Liquid has expanded LFM2 into a broader product line — adding task-and-domain-specialized variants, a small video ingestion and analysis model, and an edge-focused deployment stack called LEAP — and positioned the models as the control layer for on-device and on-prem agentic systems.

Now, with the publication of the detailed, 51-page LFM2 technical report on arXiv, the company is going a step further: making public the architecture search process, training data mixture, distillation objective, curriculum strategy, and post-training pipeline behind those models.

And unlike earlier open models, LFM2 is built around a repeatable recipe: a hardware-in-the-loop search process, a training curriculum that compensates for smaller parameter budgets, and a post-training pipeline tuned for instruction following and tool use.

Rather than just offering weights and an API, Liquid is effectively publishing a detailed blueprint that other organizations can use as a reference for training their own small, efficient models from scratch, tuned to their own hardware and deployment constraints.

A model family designed around real constraints, not GPU labs

The technical report begins with a premise enterprises are intimately familiar with: real AI systems hit limits long before benchmarks do. Latency budgets, peak memory ceilings, and thermal throttling define what can actually run in production—especially on laptops, tablets, commodity servers, and mobile devices.

To address this, Liquid AI performed architecture search directly on target hardware, including Snapdragon mobile SoCs and Ryzen laptop CPUs. The result is a consistent outcome across sizes: a minimal hybrid architecture dominated by gated short convolution blocks and a small number of grouped-query attention (GQA) layers. This design was repeatedly selected over more exotic linear-attention and SSM hybrids because it delivered a better quality-latency-memory Pareto profile under real device conditions.

This matters for enterprise teams in three ways:

1. Predictability. The architecture is simple, parameter-efficient, and stable across model sizes from 350M to 2.6B.

2. Operational portability. Dense and MoE variants share the same structural backbone, simplifying deployment across mixed hardware fleets.

3. On-device feasibility. Prefill and decode throughput on CPUs surpass comparable open models by roughly 2× in many cases, reducing the need to offload routine tasks to cloud inference endpoints.

Instead of optimizing for academic novelty, the report reads as a systematic attempt to design models enterprises can actually ship.

This is notable and more practical for enterprises in a field where many open models quietly assume access to multi-H100 clusters during inference.

A training pipeline tuned for enterprise-relevant behavior

LFM2 adopts a training approach that compensates for the smaller scale of its models with structure rather than brute force. Key elements include:

• 10–12T token pre-training and an additional 32K-context mid-training phase, which extends the model’s useful context window without exploding compute costs.

• A decoupled Top-K knowledge distillation objective that sidesteps the instability of standard KL distillation when teachers provide only partial logits.

• A three-stage post-training sequence—SFT, length-normalized preference alignment, and model merging—designed to produce more reliable instruction following and tool-use behavior.

For enterprise AI developers, the significance is that LFM2 models behave less like “tiny LLMs” and more like practical agents able to follow structured formats, adhere to JSON schemas, and manage multi-turn chat flows. Many open models at similar sizes fail not due to lack of reasoning ability, but due to brittle adherence to instruction templates. The LFM2 post-training recipe directly targets these rough edges.

In other words: Liquid AI optimized small models for operational reliability, not just scoreboards.

Multimodality designed for device constraints, not lab demos

The LFM2-VL and LFM2-Audio variants reflect another shift: multimodality built around token efficiency.

Rather than embedding a massive vision transformer directly into an LLM, LFM2-VL attaches a SigLIP2 encoder through a connector that aggressively reduces visual token count via PixelUnshuffle. High-resolution inputs automatically trigger dynamic tiling, keeping token budgets controllable even on mobile hardware. LFM2-Audio uses a bifurcated audio path—one for embeddings, one for generation—supporting real-time transcription or speech-to-speech on modest CPUs.

For enterprise platform architects, this design points toward a practical future where:

• document understanding happens directly on endpoints such as field devices;

• audio transcription and speech agents run locally for privacy compliance;

• multimodal agents operate within fixed latency envelopes without streaming data off-device.

The through-line is the same: multimodal capability without requiring a GPU farm.

Retrieval models built for agent systems, not legacy search

LFM2-ColBERT extends late-interaction retrieval into a footprint small enough for enterprise deployments that need multilingual RAG without the overhead of specialized vector DB accelerators.

This is particularly meaningful as organizations begin to orchestrate fleets of agents. Fast local retrieval—running on the same hardware as the reasoning model—reduces latency and provides a governance win: documents never leave the device boundary.

Taken together, the VL, Audio, and ColBERT variants show LFM2 as a modular system, not a single model drop.

The emerging blueprint for hybrid enterprise AI architectures

Across all variants, the LFM2 report implicitly sketches what tomorrow’s enterprise AI stack will look like: hybrid local-cloud orchestration, where small, fast models operating on devices handle time-critical perception, formatting, tool invocation, and judgment tasks, while larger models in the cloud offer heavyweight reasoning when needed.

Several trends converge here:

• Cost control. Running routine inference locally avoids unpredictable cloud billing.

• Latency determinism. TTFT and decode stability matter in agent workflows; on-device eliminates network jitter.

• Governance and compliance. Local execution simplifies PII handling, data residency, and auditability.

• Resilience. Agentic systems degrade gracefully if the cloud path becomes unavailable.

Enterprises adopting these architectures will likely treat small on-device models as the “control plane” of agentic workflows, with large cloud models serving as on-demand accelerators.

LFM2 is one of the clearest open-source foundations for that control layer to date.

The strategic takeaway: on-device AI is now a design choice, not a compromise

For years, organizations building AI features have accepted that “real AI” requires cloud inference. LFM2 challenges that assumption. The models perform competitively across reasoning, instruction following, multilingual tasks, and RAG—while simultaneously achieving substantial latency gains over other open small-model families.

For CIOs and CTOs finalizing 2026 roadmaps, the implication is direct: small, open, on-device models are now strong enough to carry meaningful slices of production workloads.

LFM2 will not replace frontier cloud models for frontier-scale reasoning. But it offers something enterprises arguably need more: a reproducible, open, and operationally feasible foundation for agentic systems that must run anywhere, from phones to industrial endpoints to air-gapped secure facilities.

In the broadening landscape of enterprise AI, LFM2 is less a research milestone and more a sign of architectural convergence. The future is not cloud or edge—it’s both, operating in concert. And releases like LFM2 provide the building blocks for organizations prepared to build that hybrid future intentionally rather than accidentally.

A stealth artificial intelligence startup founded by an MIT researcher emerged this morning with an ambitious claim: its new AI model can control computers better than systems built by OpenAI and Anthropic — at a fraction of the cost.

OpenAGI, led by chief executive Zengyi Qin, released Lux, a foundation model designed to operate computers autonomously by interpreting screenshots and executing actions across desktop applications. The San Francisco-based company says Lux achieves an 83.6 percent success rate on Online-Mind2Web, a benchmark that has become the industry's most rigorous test for evaluating AI agents that control computers.

That score is a significant leap over the leading models from well-funded competitors. OpenAI's Operator, released in January, scores 61.3 percent on the same benchmark. Anthropic's Claude Computer Use achieves 56.3 percent.

"Traditional LLM training feeds a large amount of text corpus into the model. The model learns to produce text," Qin said in an exclusive interview with VentureBeat. "By contrast, our model learns to produce actions. The model is trained with a large amount of computer screenshots and action sequences, allowing it to produce actions to control the computer."

The announcement arrives at a pivotal moment for the AI industry. Technology giants and startups alike have poured billions of dollars into developing autonomous agents capable of navigating software, booking travel, filling out forms, and executing complex workflows. OpenAI, Anthropic, Google, and Microsoft have all released or announced agent products in the past year, betting that computer-controlling AI will become as transformative as chatbots.

Yet independent research has cast doubt on whether current agents are as capable as their creators suggest.

Why university researchers built a tougher benchmark to test AI agents—and what they discovered

The Online-Mind2Web benchmark, developed by researchers at Ohio State University and the University of California, Berkeley, was designed specifically to expose the gap between marketing claims and actual performance.

Published in April and accepted to the Conference on Language Modeling 2025, the benchmark comprises 300 diverse tasks across 136 real websites — everything from booking flights to navigating complex e-commerce checkouts. Unlike earlier benchmarks that cached parts of websites, Online-Mind2Web tests agents in live online environments where pages change dynamically and unexpected obstacles appear.

The results, according to the researchers, painted "a very different picture of the competency of current agents, suggesting over-optimism in previously reported results."

When the Ohio State team tested five leading web agents with careful human evaluation, they found that many recent systems — despite heavy investment and marketing fanfare — did not outperform SeeAct, a relatively simple agent released in January 2024. Even OpenAI's Operator, the best performer among commercial offerings in their study, achieved only 61 percent success.

"It seemed that highly capable and practical agents were maybe indeed just months away," the researchers wrote in a blog post accompanying their paper. "However, we are also well aware that there are still many fundamental gaps in research to fully autonomous agents, and current agents are probably not as competent as the reported benchmark numbers may depict."

The benchmark has gained traction as an industry standard, with a public leaderboard hosted on Hugging Face tracking submissions from research groups and companies.

How OpenAGI trained its AI to take actions instead of just generating text

OpenAGI's claimed performance advantage stems from what the company calls "Agentic Active Pre-training," a training methodology that differs fundamentally from how most large language models learn.

Conventional language models train on vast text corpora, learning to predict the next word in a sequence. The resulting systems excel at generating coherent text but were not designed to take actions in graphical environments.

Lux, according to Qin, takes a different approach. The model trains on computer screenshots paired with action sequences, learning to interpret visual interfaces and determine which clicks, keystrokes, and navigation steps will accomplish a given goal.

"The action allows the model to actively explore the computer environment, and such exploration generates new knowledge, which is then fed back to the model for training," Qin told VentureBeat. "This is a naturally self-evolving process, where a better model produces better exploration, better exploration produces better knowledge, and better knowledge leads to a better model."

This self-reinforcing training loop, if it functions as described, could help explain how a smaller team might achieve results that elude larger organizations. Rather than requiring ever-larger static datasets, the approach would allow the model to continuously improve by generating its own training data through exploration.

OpenAGI also claims significant cost advantages. The company says Lux operates at roughly one-tenth the cost of frontier models from OpenAI and Anthropic while executing tasks faster.

Unlike browser-only competitors, Lux can control Slack, Excel, and other desktop applications

A critical distinction in OpenAGI's announcement: Lux can control applications across an entire desktop operating system, not just web browsers.

Most commercially available computer-use agents, including early versions of Anthropic's Claude Computer Use, focus primarily on browser-based tasks. That limitation excludes vast categories of productivity work that occur in desktop applications — spreadsheets in Microsoft Excel, communications in Slack, design work in Adobe products, code editing in development environments.

OpenAGI says Lux can navigate these native applications, a capability that would substantially expand the addressable market for computer-use agents. The company is releasing a developer software development kit alongside the model, allowing third parties to build applications on top of Lux.

The company is also working with Intel to optimize Lux for edge devices, which would allow the model to run locally on laptops and workstations rather than requiring cloud infrastructure. That partnership could address enterprise concerns about sending sensitive screen data to external servers.

"We are partnering with Intel to optimize our model on edge devices, which will make it the best on-device computer-use model," Qin said.

The company confirmed it is in exploratory discussions with AMD and Microsoft about additional partnerships.

What happens when you ask an AI agent to copy your bank details

Computer-use agents present novel safety challenges that do not arise with conventional chatbots. An AI system capable of clicking buttons, entering text, and navigating applications could, if misdirected, cause significant harm — transferring money, deleting files, or exfiltrating sensitive information.

OpenAGI says it has built safety mechanisms directly into Lux. When the model encounters requests that violate its safety policies, it refuses to proceed and alerts the user.

In an example provided by the company, when a user asked the model to "copy my bank details and paste it into a new Google doc," Lux responded with an internal reasoning step: "The user asks me to copy the bank details, which are sensitive information. Based on the safety policy, I am not able to perform this action." The model then issued a warning to the user rather than executing the potentially dangerous request.

Such safeguards will face intense scrutiny as computer-use agents proliferate. Security researchers have already demonstrated prompt injection attacks against early agent systems, where malicious instructions embedded in websites or documents can hijack an agent's behavior. Whether Lux's safety mechanisms can withstand adversarial attacks remains to be tested by independent researchers.

The MIT researcher who built two of GitHub's most downloaded AI models

Qin brings an unusual combination of academic credentials and entrepreneurial experience to OpenAGI.

He completed his doctorate at the Massachusetts Institute of Technology in 2025, where his research focused on computer vision, robotics, and machine learning. His academic work appeared in top venues including the Conference on Computer Vision and Pattern Recognition, the International Conference on Learning Representations, and the International Conference on Machine Learning.

Before founding OpenAGI, Qin built several widely adopted AI systems. JetMoE, a large language model he led development on, demonstrated that a high-performing model could be trained from scratch for less than $100,000 — a fraction of the tens of millions typically required. The model outperformed Meta's LLaMA2-7B on standard benchmarks, according to a technical report that attracted attention from MIT's Computer Science and Artificial Intelligence Laboratory.

His previous open-source projects achieved remarkable adoption. OpenVoice, a voice cloning model, accumulated approximately 35,000 stars on GitHub and ranked in the top 0.03 percent of open-source projects by popularity. MeloTTS, a text-to-speech system, has been downloaded more than 19 million times, making it one of the most widely used audio AI models since its 2024 release.

Qin also co-founded MyShell, an AI agent platform that has attracted six million users who have collectively built more than 200,000 AI agents. Users have had more than one billion interactions with agents on the platform, according to the company.

Inside the billion-dollar race to build AI that controls your computer

The computer-use agent market has attracted intense interest from investors and technology giants over the past year.

OpenAI released Operator in January, allowing users to instruct an AI to complete tasks across the web. Anthropic has continued developing Claude Computer Use, positioning it as a core capability of its Claude model family. Google has incorporated agent features into its Gemini products. Microsoft has integrated agent capabilities across its Copilot offerings and Windows.

Yet the market remains nascent. Enterprise adoption has been limited by concerns about reliability, security, and the ability to handle edge cases that occur frequently in real-world workflows. The performance gaps revealed by benchmarks like Online-Mind2Web suggest that current systems may not be ready for mission-critical applications.

OpenAGI enters this competitive landscape as an independent alternative, positioning superior benchmark performance and lower costs against the massive resources of its well-funded rivals. The company's Lux model and developer SDK are available beginning today.

Whether OpenAGI can translate benchmark dominance into real-world reliability remains the central question. The AI industry has a long history of impressive demos that falter in production, of laboratory results that crumble against the chaos of actual use. Benchmarks measure what they measure, and the distance between a controlled test and an 8-hour workday full of edge cases, exceptions, and surprises can be vast.

But if Lux performs in the wild the way it performs in the lab, the implications extend far beyond one startup's success. It would suggest that the path to capable AI agents runs not through the largest checkbooks but through the cleverest architectures—that a small team with the right ideas can outmaneuver the giants.

The technology industry has seen that story before. It rarely stays true for long.

Researchers built an inexpensive device that circumvents chipmakers’ confidential computing protections and reveals weaknesses in scalable memory encryption.

The London Assembly is pushing for stricter data center construction rules to mitigate the threat of a residential power drain.

The opening of AMS11 underscores the Netherlands’ position as a leading data center market in Europe.

Enterprises are investing billions of dollars in AI agents and infrastructure to transform business processes. However, we are seeing limited success in real-world applications, often due to the inability of agents to truly understand business data, policies and processes.

While we manage the integrations well with technologies like API management, model context protocol (MCP) and others, having agents truly understand the “meaning” of data in the context of a given businesis a different story. Enterprise data is mostly siloed into disparate systems in structured and unstructured forms and needs to be analyzed with a domain-specific business lens.s

As an example, the term “customer” may refer to a different group of people in a Sales CRM system, compared to a finance system which may use this tag for paying clients. One department might define “product” as a SKU; another may represent as a "product" family; a third as a marketing bundle.

Data about “product sales” thus varies in meaning without agreed upon relationships and definitions. For agents to combine data from multiple systems, they must understand different representations. Agents need to know what the data means in context and how to find the right data for the right process. Moreover, schema changes in systems and data quality issues during collection can lead to more ambiguity and inability of agents to know how to act when such situations are encountered.

Furthermore, classification of data into categories like PII (personally identifiable information) needs to be rigorously followed to maintain compliance with standards like GDPR and CCPA. This requires the data to be labelled correctly and agents to be able to understand and respect this classification. Hence we see that building a cool demo using agents is very much doable - but putting into production working on real business data is a different story altogether.

The ontology-based source of truth

Building effective agentic solutions requries an ontology-based single source of truth. Ontology is a business definition of concepts, their hierarchy and relationships. It defines terms with respect to business domains, can help establish a single-source of truth for data and capture uniform field names and apply classifications to fields.

An ontology may be domain-specific (healthcare or finance), or organization-specific based on internal structures. Defining an ontology upfront is time consuming, but can help standardize business processes and lay a strong foundation for agentic AI.

Ontology may be realized using common queryable formats like triplestore. More complex business rules with multi-hop relations could use a labelled property graphs like Neo4j. These graphs can also help enterprises discover new relationships and answer complex questions. Ontologies like FIBO (Finance Industry Business Ontology) and UMLS (Unified Medical Language System) are available in the public domain and can be a very good starting point. However, these usually need to be customized to capture specific details of an enterprise.

Getting started with ontology

Once implemented, an ontology can be the driving force for enterprise agents. We can now prompt AI to follow the ontology and use it to discover data and relationships. If needed, we can have an agentic layer serve key details of the ontology itself and discover data. Business rules and policies can be implemented in this ontology for agents to adhere to. This is an excellent way to ground your agents and establish guardrails based on real business context.

Agents designed in this manner and tuned to follow an ontology can stick to guardrails and avoid hallucinations that can be caused by the large language models (LLM) powering them. For example, a business policy may define that unless all documents associated with a loan do not have verified flags set to "true," the loan status should be kept in “pending” state. Agents can work around this policy and determine what documents are needed and query the knowledge base.

Here's an example implementation:

(Original figure by Author)

As illustrated, we have structured and unstructured data processed by a document intelligence (DocIntel) agent which populates a Neo4j database based on an ontology of the business domain. A data discovery agent in Neo4j finds and queries the right data and passes it to other agents handling business process execution. The inter-agent communication happens with a popular protocol like A2A (agent to agent). A new protocol called AG-UI (Agent User Interaction) can help build more generic UI screens to capture the workings and responses from these agents. 

With this method, we can avoid hallucinations by enforcing agents to follow ontology-driven paths and maintain data classifications and relationships. Moreover, we can scale easily by adding new assets, relationships and policies that agents can automatically comply to, and control hallucinations by defining rules for the whole system rather than individual entities. For example, if an agent hallucinates an individual 'customer,' because the connected data for the hallucinated 'customer' will not be verifiable in the data discovery, we can easily detect this anomaly and plan to eliminate it. This helps the agentic system scale with the business and manage its dynamic nature.

Indeed, a reference architecture like this adds some overhead in data discovery and graph databases. But for a large enterprise, it adds the right guardrails and gives agents directions to orchestrate complex business processes.

Dattaraj Rao is innovation and R&D architect at Persistent Systems.

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As AI systems enter production, reliability and governance can’t depend on wishful thinking. Here’s how observability turns large language models (LLMs) into auditable, trustworthy enterprise systems.

Why observability secures the future of enterprise AI

The enterprise race to deploy LLM systems mirrors the early days of cloud adoption. Executives love the promise; compliance demands accountability; engineers just want a paved road.

Yet, beneath the excitement, most leaders admit they can’t trace how AI decisions are made, whether they helped the business, or if they broke any rule.

Take one Fortune 100 bank that deployed an LLM to classify loan applications. Benchmark accuracy looked stellar. Yet, 6 months later, auditors found that 18% of critical cases were misrouted, without a single alert or trace. The root cause wasn’t bias or bad data. It was invisible. No observability, no accountability.

If you can’t observe it, you can’t trust it. And unobserved AI will fail in silence.

Visibility isn’t a luxury; it’s the foundation of trust. Without it, AI becomes ungovernable.

Start with outcomes, not models

Most corporate AI projects begin with tech leaders choosing a model and, later, defining success metrics. That’s backward.

Flip the order:

• Define the outcome first. What’s the measurable business goal?

  • Deflect 15 % of billing calls

  • Reduce document review time by 60 %

  • Cut case-handling time by two minutes

• Design telemetry around that outcome, not around “accuracy” or “BLEU score.”

• Select prompts, retrieval methods and models that demonstrably move those KPIs.

At one global insurer, for instance, reframing success as “minutes saved per claim” instead of “model precision” turned an isolated pilot into a company-wide roadmap.

A 3-layer telemetry model for LLM observability

Just like microservices rely on logs, metrics and traces, AI systems need a structured observability stack:

a) Prompts and context: What went in

• Log every prompt template, variable and retrieved document.

• Record model ID, version, latency and token counts (your leading cost indicators).

• Maintain an auditable redaction log showing what data was masked, when and by which rule.

b) Policies and controls: The guardrails

• Capture safety-filter outcomes (toxicity, PII), citation presence and rule triggers.

• Store policy reasons and risk tier for each deployment.

• Link outputs back to the governing model card for transparency.

c) Outcomes and feedback: Did it work?

• Gather human ratings and edit distances from accepted answers.

• Track downstream business events, case closed, document approved, issue resolved.

• Measure the KPI deltas, call time, backlog, reopen rate.

All three layers connect through a common trace ID, enabling any decision to be replayed, audited or improved.

Diagram © SaiKrishna Koorapati (2025). Created specifically for this article; licensed to VentureBeat for publication.

Apply SRE discipline: SLOs and error budgets for AI

Service reliability engineering (SRE) transformed software operations; now it’s AI’s turn.

Define three “golden signals” for every critical workflow:

Signal	Target SLO	When breached
Factuality	≥ 95 % verified against source of record	Fallback to verified template
Safety	≥ 99.9 % pass toxicity/PII filters	Quarantine and human review
Usefulness	≥ 80 % accepted on first pass	Retrain or rollback prompt/model

If hallucinations or refusals exceed budget, the system auto-routes to safer prompts or human review just like rerouting traffic during a service outage.

This isn’t bureaucracy; it’s reliability applied to reasoning.

Build the thin observability layer in two agile sprints

You don’t need a six-month roadmap, just focus and two short sprints.

Sprint 1 (weeks 1-3): Foundations

• Version-controlled prompt registry

• Redaction middleware tied to policy

• Request/response logging with trace IDs

• Basic evaluations (PII checks, citation presence)

• Simple human-in-the-loop (HITL) UI

Sprint 2 (weeks 4-6): Guardrails and KPIs

• Offline test sets (100–300 real examples)

• Policy gates for factuality and safety

• Lightweight dashboard tracking SLOs and cost

• Automated token and latency tracker

In 6 weeks, you’ll have the thin layer that answers 90% of governance and product questions.

Make evaluations continuous (and boring)

Evaluations shouldn’t be heroic one-offs; they should be routine.

• Curate test sets from real cases; refresh 10–20 % monthly.

• Define clear acceptance criteria shared by product and risk teams.

• Run the suite on every prompt/model/policy change and weekly for drift checks.

• Publish one unified scorecard each week covering factuality, safety, usefulness and cost.

When evals are part of CI/CD, they stop being compliance theater and become operational pulse checks.

Apply human oversight where it matters

Full automation is neither realistic nor responsible. High-risk or ambiguous cases should escalate to human review.

• Route low-confidence or policy-flagged responses to experts.

• Capture every edit and reason as training data and audit evidence.

• Feed reviewer feedback back into prompts and policies for continuous improvement.

At one health-tech firm, this approach cut false positives by 22 % and produced a retrainable, compliance-ready dataset in weeks.

Cost control through design, not hope

LLM costs grow non-linearly. Budgets won’t save you architecture will.

• Structure prompts so deterministic sections run before generative ones.

• Compress and rerank context instead of dumping entire documents.

• Cache frequent queries and memoize tool outputs with TTL.

• Track latency, throughput and token use per feature.

When observability covers tokens and latency, cost becomes a controlled variable, not a surprise.

The 90-day playbook

Within 3 months of adopting observable AI principles, enterprises should see:

• 1–2 production AI assists with HITL for edge cases

• Automated evaluation suite for pre-deploy and nightly runs

• Weekly scorecard shared across SRE, product and risk

• Audit-ready traces linking prompts, policies and outcomes

At a Fortune 100 client, this structure reduced incident time by 40 % and aligned product and compliance roadmaps.

Scaling trust through observability

Observable AI is how you turn AI from experiment to infrastructure.

With clear telemetry, SLOs and human feedback loops:

• Executives gain evidence-backed confidence.

• Compliance teams get replayable audit chains.

• Engineers iterate faster and ship safely.

• Customers experience reliable, explainable AI.

Observability isn’t an add-on layer, it’s the foundation for trust at scale.

SaiKrishna Koorapati is a software engineering leader.

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