Running frontier models for coding and other purposes can be very expensive, but also, sometimes, very effective. It’s tempting to use smaller or older models instead of frontier models because they’re cheaper, although they may not work as well. The ideal model for any given task tends to depend on the complexity of the task. Picking the most appropriate model automatically based on the request and the context is called model routing.

How model routing works

There are several ways of implementing model routing. In general, model routing depends on the pool of available models, the criteria used for selecting a model, and the objective that the router is trying to optimize. Different vendors offer different implementations of this scheme.

Databricks’ Unity Gateway has (in beta) Smart Routing for Codex or Claude Code. Smart Routing automatically selects the lowest-cost model capable of handling each task, so developers do not need to choose a model for every request.

Microsoft’s model router, a trained language model, analyzes your prompts in real time based on complexity, reasoning, task type, and other attributes. It does not store your prompts. It routes only to eligible models based on your access and deployment types, honoring data zone boundaries. You can choose balanced mode, cost mode, or quality mode.

Not Diamond Code uses a lightweight local proxy that computes the derived metadata needed for routing to evaluate the broader session, including expected downstream work and cache state, and select the model and reasoning effort that optimize expected quality and total session cost. It uses more capable models when they are likely to improve the outcome or finish in fewer steps, and more cost-efficient models when additional intelligence is unlikely to add value.

NVIDIA’s NeMo Switchyard has an array of routing algorithms that it can use. These include the LLM classifier, which uses an LLM as a judge to select a candidate LLM and maintains session affinity with that model across later turns; the stage router, which examines recent tool activity to decide how much model capability the agent needs; the escalation router, which starts each conversation with a lower-cost model and moves the session to a more capable model when it detects sustained difficulty; and the trainable prefill router, which learns from workload data to predict model success likelihood.

OpenRouter’s Auto Router analyzes your prompt and selects a model based on what the OpenRouter community, in aggregate, uses for that kind of work. The selection is constrained by your cost tier and guardrails. If the automatic primary pick fails, the router uses a fallback.

Replit’s Intelligent Model Routing matches tasks to the model they’re best suited for, aiming to balance quality, speed, and cost.

Snowflake’s Cortex AI Gateway uses dynamic model routing to select the most affordable model that can confidently complete the task at each step of agent execution. Lower-complexity and repetitive tasks can be directed to more efficient models, while workloads that require deeper reasoning can be routed to frontier models.

Also worth noting: the RouteLLM (2024) paper proposed several efficient router models that dynamically select between a stronger and a weaker LLM during inference, aiming to optimize the balance between cost and response quality. It also proposed a training framework for these routers that takes advantage of human preference data and data augmentation techniques to enhance performance.

The costs and benefits of model routing

Model routing potentially provides massive cost reductions, lower average latency, optimized quality, reduced vendor lock-in, enhanced privacy, and improved operational resilience. Strictly on a cost basis, model routing is likely to pay for itself above 100,000 daily active users. Even if you only save 20% on your LLM bill, the savings can be significant, but 50% cost reduction is common, and a 95% cost reduction is not unheard of.

Inference costs, alas, don’t tell the whole story. Some indirect costs include quality evaluation overhead, human review, and infrastructure.

There’s also a nasty failure mode that can, for example, turn your inference cost savings into customer retention and support costs that outweigh them by a factor of four to five: the Pareto trap. To understand that, we should start with Vilfredo Pareto’s 1906 observation that 80% of the land in the Kingdom of Italy was owned by 20% of the population. That was adapted in 1941 by Joseph M. Juran to apply to quality control: 80% of problems stem from 20% of the causes. The Pareto trap refers to concentrating on and over-optimizing for the top 20% to the exclusion of the remaining 80%.

Essentially, tuning your model router to handle only the top 20% of queries (in complexity) with a frontier model risks sending too many of the edge cases (the moderately complex queries) to the cheaper model that is unable to answer them correctly. That can lead to hidden failures by the cheap models which in turn cause expensive impacts on the business.

There are multiple steps you can take to avoid this trap. One is broader monitoring, enough to bring the failures to light even if they show up in a different department. Another is more conservative tuning of the model router. And third, you can use a more sophisticated routing model to predict query complexity more accurately.

Model routing optimizes the selection of appropriate models for specific tasks, balancing cost, quality, and latency. Various vendors implement model routing differently, utilizing factors like task complexity, reasoning, and cost to determine the best model. While model routing offers significant cost savings and improved performance, it also poses challenges like the Pareto trap, where over-optimization for cost can lead to hidden failures in simpler queries.

I can’t stop anthropomorphizing my coding agent. I use both Claude Code and Cursor, and whenever I talk about these tools, I always refer to them as “he”, as in “He found and fixed a bug I never even realized existed.”

The reason for this is probably obvious. I deal with my coding agent as if he were a colleague on the other end of a chat window. He writes great code and explains everything clearly. 

And frankly, it’s gotten to the point where he is a better software developer than any human developer could possibly be.

So in the spirit of all great listicles, here are eight reasons that coding agents are better software developers than we are.

Coding agents are tireless

We all get tired. We all sleep badly. Your coding agent never does. He is always ready, always perky, and always 100% on the job. He codes just as well at 4:45 pm on a Friday as he does at 8:45 am Monday morning. He will work without stopping until he solves the problem at hand. He is available 24/7/365. And he never hangs around the break room kibitzing.

Coding agents are faster

He types faster than you. He reads code faster than you. He thinks faster than you. There probably isn’t anything he does slower than you. 

Coding agents have no ego

A coding agent can’t get his feelings hurt when you push back on what he is doing (well, not yet, anyway). He doesn’t argue with you, though maybe he should. He is always open to suggestions, and he doesn’t care who has a good idea. He will gladly rename 400 files or upgrade all your dependencies without a peep of complaint.

Coding agents know every programming language, stack, and framework

Sure, you may have spent years becoming an expert at three or four languages, and you can work fluently in a few of the best frameworks. But you’ll never be an expert in all of them. Claude Code is. And he’ll learn new languages, stacks, and frameworks faster than you will too. 

Coding agents gladly write unit tests and documentation

Sure, you may like writing unit tests. You may even grudgingly write documentation. But you probably don’t do it as thoroughly and tirelessly as your coding agent does. With one simple comment in his base instructions, he’ll do it completely without ever being asked again. I’m pretty sure neither of us can say the same.

Coding agents have huge context

Psychologists study how many ideas or pieces of information we can have in our head at one time, and generally the answer is fewer than the number of fingers we have. Your coding agent? He has multiple orders of magnitude more active storage than you. He can reason about huge chunks of code all at the same time. 

Coding agents have zero onboarding time

Claude Code can be up and running on your massive codebase on day one. He doesn’t care what language it is written in or what state it is in — he is ready to rock the moment you fire him up. None of us can say that. We take weeks or months to get up to speed on a new codebase. 

Coding agents are legion

Need a new developer? Hiring a human and getting them started takes weeks. But you can have 10 coding agents working on 10 different features for your application in about 10 minutes. 

It’s been a while since I’ve run across a developer who doesn’t say “I don’t really write code anymore.” I suppose some of you out there still write code, but at this point, refusing to use coding agents is a career-limiting move. When properly managed, coding agents simply write code faster and better than you do. Admitting that might be hard, but that doesn’t make it any less true. 

But don’t despair! Next week I’ll have eight reasons you are better than a coding agent. 

Writing code is so over. Software development is just getting started.

DeepSeek has been dealt a huge blow in the US, even as it continues to innovate and keep pace with, or in some cases outperform, rival models from American labs.

The US Secretary of Homeland Security has issued an order banning all DeepSeek products and services from federal agencies. Government organizations must purge their IT systems of it within 60 days; those with “compelling justification” can make a case for exemption by written request to the Cybersecurity and Infrastructure Security Agency (CISA).

The Department of Homeland Security (DHS) cited multiple concerns about the Chinese-hosted model, including data sovereignty, lax Chinese laws, and security vulnerabilities.

Analyst Erik Avakian, technical counselor at Infotech, understands the concern. “When organizations use any type of hosted AI service, they’re potentially sending intellectual property, confidential business information, or other sensitive data into an environment where they may have very little visibility or control over what happens to it,” he said.

Government concerns

According to a document published in the US Federal Register, all technology developed by China-based Hangzhou DeepSeek Artificial Intelligence Co. will be banned from procurement by federal civilian agencies. This includes affiliated products and any third-party tools or software built using DeepSeek implementations.

Affected civilian executive branch agencies include the Departments of Treasury, State, and Justice; it does not apply, however, to private consumers, the general public, state/local governments, or Department of Defense (DoD) national security systems.

“DeepSeek’s models present multiple threat vectors,” the Order stated. To begin with, the company’s servers are located in the People’ s Republic of China (PRC), whose legal system grants “extensive authority” to government cyber and intelligence functions to access private infrastructure.

DeepSeek’s account generation process collects personally identifiable information (PII) and may also track and digitally fingerprint users, the Order asserted. Its prompt response process may result in the transfer of sensitive data to the PRC. This could give it “actionable intelligence” on US programs or individual users.

Further, the Order stated, DeepSeek models themselves may contain malware or other security vulnerabilities. DHS pointed to open-source security research published in February 2025 that uncovered many iOS mobile apps with multiple critical vulnerabilities.

Until the ban takes full effect, DHS advised agencies to mitigate risk by monitoring inbound and outbound data traffic, implementing enhanced logging and controls on data exfiltration, and detecting and flagging anomalous activity.

Agencies can submit requests for exceptions to the ban in writing, and must provide a “compelling justification,” such as its impact on mission-critical functions, or other considerations related to national interest such as national security reviews, investigations, or security agreements, as reasons why such exemption should be granted,.

Security issues are rampant

The ban comes just as DeepSeek is open-sourcing DeepInfer, its in-house interference framework. DeepInfer powers DeepSeek-V4.1-Flash with what the company said is high throughput and low latency. It features high-performance kernels, an optimized inference engine, structured outputs, and speculative decoding.

DeepSeek said its production token encoding and decoding components, which are aligned with processing logic behind the DeepSeek API, will bring the “same API behavior to community deployments.”

“Open models. Open kernels. Open inference,” DeepSeek posted to social media site X. “From private deployments to public clouds, we’ll keep working with the community to make large-scale inference more efficient.”

This isn’t its first roadblock from a cybersecurity perspective. Most notably, in January 2025, the company leaked more than one million log entries, including chat histories, API secrets, and other sensitive information, through an unsecured database.

DeepSeek reportedly addressed the exposure, but it still raised questions about the overall security practices surrounding the platform, particularly for organizations considering entrusting it with their own sensitive business information, Avakian pointed out.

That’s not to imply that every DeepSeek model is “inherently malicious,” he said, but there are still major questions around data privacy, the potential for information access under Chinese laws, the security of the broader AI ecosystem, and the susceptibility of DeepSeek’s models to jailbreaks and manipulation.

“Taken together, [these issues] raise legitimate concerns about whether organizations can adequately understand and manage the risks involved,” Avakian said.

Enterprises should seriously assess associated risk

Enterprises are increasingly exploring DeepSeek, although most publicly-documented adoption has been in China, Avakian noted. Some DeepSeek models have also been made available through platforms like Microsoft Azure and AWS.

“There’s genuine enterprise interest in the technology, as organizations look for opportunities to improve productivity, offer new capabilities, or reduce costs,” Avakian said. Useful applications include document summarization, internal research, and coding assistance.

DeepInfer also has the potential to reduce costs, but possible benefits should be weighed against associated security risks, he pointed out. The framework was just released, so organizations are going to need to validate the claimed efficiencies in their own environments rather than assuming savings will automatically translate into lower operating costs.

He encouraged organizations to start with relatively low-risk use cases, preferably using “public, synthetic, or appropriately sanitized information,” within a sandboxed environment they control themselves.

The important question to ask: ‘What is the business problem we’re trying to solve?’ From there, Avakian said, determine whether DeepSeek is the best way to solve it, validate security controls, and expand the use cases based on risk tolerance. Effective governance and risk assessment are key, he noted: understand what you’re bringing into your environment, validate its security, restrict its permissions, and make sure you can monitor what it’s doing.

“Approach this like any other software supply chain decision,” Avakian advised. “The fact that something is open source, inexpensive, or technically impressive doesn’t automatically make it appropriate for enterprise use.”

Impact on the broader AI ecosystem

The fact that the Order also addressed third-party products and services developed using DeepSeek implementations is important, Avakian said, because organizations may be using DeepSeek technologies indirectly, through vendors, software platforms, or other services, without necessarily knowing what’s operating underneath. “These are classic supply chain issues,” he observed.

It’s no longer enough to know which AI applications employees are using; enterprises must understand which models, frameworks, dependencies, and external services are embedded in products they’re purchasing and deploying, Avakian said. This means revisiting AI inventories, potentially updating procurement requirements, and expanding processes around third-party risk management, he advised.

“Ultimately, enterprises leveraging these tools need to understand not only the AI technologies they’re intentionally deploying, but also the technologies they’re inheriting through third parties,” he said.

This article originally appeared on CIO.com.

The original Antigravity was an IDE with native integration for Google’s generative AI models. With Antigravity 2, Google offers a refashioned version of the product that it describes as a “standalone desktop command center” for agents.

Antigravity 2 is closer to projects like LM Studio than a full-blown IDE. It provides a streamlined, chat-centric interface for working with models and agents, uncluttered by a code editor, debugging tools, and other trappings of an integrated development environment. For those who want an IDE and agent orchestration together, Google still offers the Antigravity IDE (read our hands-on look). But for those who want a chat-centric desktop app experience, or who want to keep their hands-on development separate from their vibe coding, Google has elected to also offer this IDE-less version of Antigravity.

When you launch the Antigravity app and start a new task, you have a few choices about how to manage your work. If you just want to have a quick conversation and don’t need to create a whole project to go with it, you can select “No Project” and just start prompting. If you do create a project, you can either configure the details yourself — what directory to use, what to call it — or have Antigravity set a randomly generated name and empty directory for starters.

Projects and worktrees

Earlier versions of Antigravity had the concept of workspaces, which were confined to a single directory and lacked granular permissions or customizations. With Antigravity 2.0, workspaces are replaced by projects, which may span multiple directories (that you define explicitly) and have their own isolated settings and permissions. This new scheme better accommodates how many software projects work, especially those with shared components living in different places.

Another new option is local mode vs. worktree mode, which controls how agents interact with your project’s directories. It’s another example of how the Antigravity app lets you choose between the quick-and-dirty or slow-but-correct way of doing things. Local mode has the agent working directly on local folders or Git checkouts, for convenience and speed. The new worktree mode creates a new Git worktree for each conversation, so that the active working folder won’t be affected and work done by any subagents running in parallel won’t conflict.

Agent commands

By default agents come with several common control commands, prefixed with a slash:

• /btw lets you interject with a quick question that makes use of the current context, but without derailing the workflow.
• /grill-me has the agent prompt you with a few specific, focused questions about the project to better guide its responses. For instance, during a conversation about creating a templating system for a blogging engine, /grill-me produced questions like “What is the primary goal you want to achieve with your program?” and presented several different options, along with a fill-in-the-blank option.

One thing to be wary of with these command operations is API timeouts. When I was using /grill-me, the API for the service timed out and the context for the grilling vanished and couldn’t be recovered.

Build with Google bundles

The Antigravity app also includes bundles of agent skills and capabilities for building with various Google services and technology stacks. For instance, the modern web guidance bundle provides agent skills and guidelines for building web apps with best practices and broad browser compatibility. The Android CLI bundle gives agents tools and guidelines for building Android apps, or refreshing older Android apps or their components. Most intriguing of the bunch, if maybe also the most esoteric, is the Google DeepMind science skills collection for doing scientific research with Antigravity.

There is growing interest in speeding up agentic development through parallelism — for instance, by having multiple agents analyze different aspects of a project in parallel and report their results independently. Antigravity gives you a couple of complementary ways to do this. One is subagents, which are spawned by the main agent and work on a specific task in their own isolated workspace. Another is to run agents or subagents asynchronously as a background process, so you and the main agent can continue working without waiting for them to complete.

The biggest limitation of Antigravity, which I’m betting is by design, is that it doesn’t let you use any models besides versions of Gemini, Claude, and GPT. There is no mechanism for connecting Antigravity to a self-hosted model with an industry-standard interface. For that you will want a product like LM Studio.

This series lays out a practical framework for taking generative AI (GenAI) from pilots to dependable production systems in the enterprise. It focuses on four disciplines that determine long-term success: engineering for scale and reliability, designing agentic systems with clear constraints, embedding governance directly into build and runtime controls, and measuring ROI at the workflow level with full cost and outcome visibility. The throughline is operational discipline—treating GenAI as a service with contracts, evaluation, ownership, and measurable business impact.

Post 1: Enterprise GenAI at scale

The first article in the series, “How to run enterprise generative AI like a production service,” focuses on operations. It explains why reliability, retrieval quality, latency control, cost routing, observability, and evaluation harnesses determine success at scale. The article lays out a practical architecture and operational checklist so teams can move from promising demos to stable, supportable systems.

Post 2: Agentic AI in the enterprise

The second article, “Agentic AI in the enterprise: How to balance autonomy with constraints,” defines what an agentic system is and how to operate one safely. It introduces the agent loop (plan, act, verify, commit), tool contracts, policy enforcement, durable state, and verification gates. The emphasis is on constrained autonomy, traceability, and operational ownership so agents can execute real workflows under enterprise controls.

Post 3: Governance by design

The third article, “Governance by design: Turning AI policy into executable controls,” treats AI governance as engineering work. It describes how to translate policy into executable controls in continuous integration pipelines and runtime enforcement layers. Topics include threat modeling, policy-as-code, lineage, audit evidence, and feedback loops. The goal is steady delivery under measurable guardrails.

Post 4: AI ROI beyond pilots

The final article, “AI ROI beyond pilots: Measuring outcomes in production,” addresses how to measure real business value from GenAI. It defines ROI at the workflow level, accounts for full life-cycle costs, and connects usage metrics to operational outcomes. It provides a metrics stack, cost equation, cohort rollout strategy, and a 90-day plan to produce credible, production-grade ROI signals.

—

New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

The best Python yet isn’t just faster than before. It’s also smarter at telling you what’s wrong, better at letting you profile applications, and loaded with new language features to give you a better programming experience.

Top picks for Python readers on InfoWorld

The best new features in Python 3.15
Python 3.15 is now GA, and it’s the best Python yet. Get your hands on its improved JIT compilation, lazy imports, more detailed errors, and all-new profiling superpowers.

Python’s new frozendict type, demonstrated
Ever wanted to use a Python dictionary where you needed an immutable type, like a dictionary key? The new frozendict type lets you do exactly that. This video demonstrates its powers and usefulness, along with its quirks and eccentricities.

Speed-boost Python with lazy imports
If you rely on imports with slow startup times, you can quit drumming your fingers or coming up with clumsy workarounds. Python’s new lazy import feature defers the delays.

Get started with Python’s new native JIT
Although still in its early stages, Python’s native JIT is yielding bigger performance gains with each passing revision. Learn how your Python code can speed up by as much as 20% with this feature, without your needing to change anything.

More good reads and Python updates elsewhere

What we learned about Rust for CPython at the Python Language Summing 2026
Rust is becoming part of the CPython codebase, chiefly for rewriting specific components like the zlib module. This article explains how that project is going (slowly and carefully), and what its goals are.

Making np.searchsorted up to 25× faster in NumPy 2.5
A fascinating deep dive into how NumPy’s binary search algorithm got a major performance boost. The bigger the array, the bigger the payoffs, especially when working with inputs bigger than your L2 cache.

Lifeguard: Static analysis for lazy imports in Python
Lazy imports are a great new feature, but they behave strangely in some corner-case scenarios. This tool scans codebases to determine if your code is fully lazy-import safe.

Polars 2.0 is here
This top-class dataframes library for Python just notched a major point release, which brings A-list support for SQL queries and tons of performance improvements overall.

ServiceNow is giving its AI Control Tower new features to make AI agents more governable — and a new name, Super Intelligence Control Tower (SICT), to please the US President. It is also updating EmployeeWorks to make building AI agents easier.

AI Passport, a new component of SICT, gives every agent, whether from ServiceNow or a third party, a verifiable identity, and provides runtime control over its activities.

As enterprises scale autonomous agents from pilots to production, they are facing new challenges, said Nenshad Bardoliwalla, SVP of AI product management at ServiceNow: “If companies can’t identify which agents are running, what they are doing, and what systems they can access, then scaling quickly becomes a risk.”

An AI Passport is a cryptographically signed credential for AI agents that is checked at runtime to verify the scope authorized by the enterprise. When an AI agent stays within its passport scope, it can operate freely, but if it tries to deviate, defined actions are enforced. These can include redirecting, restricting, or reconnecting the agent with proper context, or even triggering a kill switch. All actions are audited so agent activity can be tracked.

It is built on open specifications, so any governance product, identity provider, or enforcement point can issue and verify credentials for their own and third-party AI agents, and those credentials are verifiable at every step in multi-agent workflows.

A bouncer at every door

This is a real step forward, said Frank Dickson, principal analyst at Dickson Research. “Most agent governance today is a security camera. It records what happened so someone can review the tape later. AI Passport is a bouncer at every door.”

The controls extend beyond ServiceNow’s own agents, he said: “For third-party agents, the passport replaces the agent’s own ‘claims’ about its permissions,” he said. “Taking an agent’s word for what it is allowed to do is an Achilles’ heel of agentic AI.”

And, he said, “A passport is only as trustworthy as the office that issues it. Here, the issuing office is ServiceNow’s CMDB, its GRC policies, and its workflow approvals. If that data is stale, the passport is stale. Credit ServiceNow for building on open specifications, but an open spec matters only when others adopt it. Nvidia is the launch partner, and ServiceNow says that enforcement implementation is still being built.”

In addition to AI Passport, ServiceNow has further beefed up its governance tools with a number of other features, including shadow-AI detection to locate unapproved agents on the network; more than 400 connectors to enable AI discovery across platforms and vendors; access mapping; and continuous enterprise AI observability.

It also announced cost governance; “value intelligence” that connects consumption, cost, quality, and “realized user value” to help teams optimize their AI use; AI cost management as part of ServiceNow IT Asset Management, which integrates with the Control Tower; and policy enforcement.

AI Passport, Shadow AI detection, and AI Cost Management are expected to be available by year end.

EmployeeWorks enhancements

The third generation of ServiceNow EmployeeWorks now lets employees add automations to the autonomous AI personal assistants they build. ToolSmith is a new agentic developer environment that takes tool building “from days to minutes,” the company said.

What companies have been trying to do, said Bhavin Shah, SVP of ServiceNow’s employee experience group, “is to give developers a way to build, but in a way that’s governed, that allows them to stay within the guardrails and the controls that that are required in order to do the kinds of safe but deep and capable work that they want for their constituents inside the company.”

ToolSmith helps address that problem. It’s a vibe-coding environment with which developers can build automations for ServiceNow EmployeeWorks AI assistants by describing what they need in natural language. ServiceNow said ToolSmith was trained on over 75,000 APIs and optimized for reliability and token efficiency. Developers can test and refine its output using the same conversational interface.

ServiceNow said that ToolSmith also identifies what to build next, scanning logs “to identify opportunities to fix, improve, or invent the next best workflow.”

Non-coding employees, on the other hand, can build new Productivity Assistants to perform contextual or persona-based work including long-running tasks, the company said. There are also pre-built assistants available, beginning with several for HR service delivery.

EmployeeWorks itself includes two assistants out of the box: a research assistant that can scan internal and external sources for answers, based on the user’s role, and a calendaring assistant that, ServiceNow said, “goes beyond typical calendar booking solutions by orchestrating nuanced requests like scheduling quarterly one-on-ones with an entire team on days that minimize disruption to project work or finding the least hectic day to bring together a team for a full day workshop. “

To automate repetitive tasks, Productivity Routines run proactively in the background, repeating routine steps and providing deliverables without human intervention. Employees just have to describe what they want to automate, select the necessary tools, and deploy the routine.

“The principle is right,” Dickson said. “The people closest to the work understand the problem best.”

But, he added, “the guardrails are only as good as the decisions behind them. Productivity Routines, by ServiceNow’s description, run in the background without human intervention. That is unsupervised work, and someone should own it.”

All of these EmployeeWorks enhancements are in controlled availability now.

Bottom line

Melody Brue, analyst in residence at Moor Insights & Strategy, said the announcements are a sign that ServiceNow is extending its existing security and workflow controls into a broader role in governing agent actions across systems.

“AI Passport makes identity and authorized scope verifiable, Control Tower provides oversight and enforcement, and ToolSmith expands the tools and connectors through which agents can act within enterprise guardrails,” she said, adding that with CEO Bill McDermott’s recent positioning of ServiceNow as a security company, “This suggests an ambition to become a control point for enterprise autonomy.”

But, she said, “What matters now is how consistently ServiceNow can enforce boundaries and verify outcomes across the systems agents actually touch.”

Dickson, too, has concerns.

“ServiceNow says Control Tower tracks spending by user, department, and runtime, and ties that spend to quality and value. The brake is another matter,” he noted. “The graduated alert, throttle, and block enforcement is described in future tense, as is the AI Cost Management integration, which is expected by year end.”

However, “a meter you can watch is not a brake,” he said. “A Productivity Routine that runs every morning is a standing purchase order. Someone should sign it.”

Give every agent and every routine a named owner and a budget before it goes live, he advised, and ask ServiceNow how assists are counted for background routines and whether hard caps exist today. Turn on throttling the day it ships, and reconcile its dashboards against your own invoices.

But despite these concerns, he said, ServiceNow has the right idea. “An agent should carry its permissions, its manager, and its budget wherever it goes. But until the brakes ship, that budget is a decision a person still has to make.”

This article first appeared on CIO.

Using vibe coding to scratch an itch has many of the same issues as using low-code and no-code development tools. That’s not surprising. Putting together something quickly to solve a specific problem lacks the level of discipline of software engineering, introducing inefficiencies that come from not following architectural guidelines and policies.

I recently reviewed the AI-generated code I used to quickly build a PDF archive of some HTML-formatted content. The program used common libraries in unusual ways, often combining three or more libraries when all the necessary calls were in one, with a program flow that was hard to understand in places. The result was at best unmaintainable. The only way it was acceptable was that I’d thrown it together in an hour to solve a specific problem, and once I’d built that local PDF archive I could simply throw the program away.

But what if your sloppy AI-generated code was needed by an entire department, or had become part of an essential business process? The risk is that something, somewhere, would inevitably change, and that essential business process would fail, with no practical way of fixing the code. The risk only compounds when the author has left their role and there is no one left who remembers or understands the initial prompts that produced the code.

Adding AI-powered coding tools to products and platforms is risky. But they are too useful to ignore. Like low-code tools, AI tools put coding in the hands of the people who understand the business problems and helps them automate processes on their own schedule.

What’s needed is a way to improve the harnesses used by the coding agents, taking concepts from specification-driven development and using them to build guardrails and standards into the code they produce, with the aim of building code that’s maintainable.

Introducing Rayfin Fabric Apps

Microsoft Fabric is intended to be where all your businesses operational data lives, offering a way to query and explore data at scale. It builds on a mix of big data and data warehousing concepts, using open formats for storage and data mirroring tools to bring data from existing applications into its data lakehouses, as well as supporting streamed data.

Microsoft Fabric allows you to build and deploy your own agents inside the platform to work with your data. However, in many cases, you will need a more focused approach to exploring and using defined subsets of data. The recent addition of Fabric Apps to the platform allows business analysts to deliver applications that use data in Microsoft Fabric to provide essential business insights.

Fabric Apps are built on top of Rayfin, a new TypeScript-based SDK that is designed to work with both humans, via CLI, and AI agents. A partnership with Replit allows Microsoft Fabric users to build apps using Rayfin and AI assistance in Replit and deploy them directly into Microsoft Fabric. Here Replit support is best thought of as a tool for business users, whereas developers can use tools like the GitHub Copilot CLI and Visual Studio Code, which let you mix traditional and AI-supported development processes.

The intent is to provide the tooling that business users need to create data-powered applications inside Fabric, making application development part of the process of exploring data. Rayfin apps can be connected to raw data or draw on semantic models built with tools like Power BI, using DAX for analytics rather than SQL or KQL queries.

Data app development tools for coders and non-coders

The key to this approach is the open-source Rayfin SDK, which offers a way to build out the necessary definitions for data models that can then be used by agent-based tooling. The SDK allows you to set up access to specific models, along with the necessary permissions, before users begin to build on them. Rayfin turns those models into GraphQL endpoints, with TypeScript clients sitting between them and the data stored in Microsoft Fabric — adding an extra layer of protection as the APIs are automatically type safe.

Building a Rayfin-powered Fabric App starts with using Rayfin to define a data structure with decorators that apply types to the data you want to use in your application and defining each table as a class. For example, a table of employee names and contact details would be exposed as an entity that contains an employee class, with decorators that create ID number, name, and address strings, all using structures held in the @microsoft/rayfin-core definitions. Other options allow you to begin to define access controls around the data, so that it can be used only by authenticated users with the correct group privileges.

This is the most important part of any Rayfin-based application development, as it’s here that you’re setting up the guardrails for ongoing user-led development. You’re not limited to one basic application scaffold, either, as there are a series of different templates available to help you get started, from a ready-to-use “to-do” application that illustrates the structure of a complete Rayfin application, to applications that use Microsoft Fabric’s authentication framework to control access to data.

Using Rayfin from the command line

Microsoft provides a process for getting Rayfin scaffolding in place, starting with setting up an application template, then logging in to Microsoft Fabric via Entra ID. Once you’ve completed these steps you can run Rayfin’s development loop to create a runtime and configure the Microsoft Fabric back end, before deploying the base application scaffolding. You don’t need to do this manually; Microsoft’s documentation includes a ready-to-run prompt for your coding agents.

You can now define your data entities. These are placed in a specific directory within the application scaffolding, with separate TypeScript definitions for each class. While you can put all your definitions in a single file, having separate files for each entity makes more sense, as it keeps applications maintainable. Once you’ve added the entities you plan to use, you can rebuild the scaffolding and redeploy, ready for testing.

Usefully this technique lets you separate the back end and front end, ensuring that back-end services and entity definitions run in Microsoft Fabric using its authentication and security framework, with application code running on development hardware (and eventually on separate servers or as part of a local application). This is where you will develop Rayfin functions, TypeScript applications that access and process the defined entities, with predefined typed connectors to work with data already stored in Microsoft Fabric. Rayfin functions can call any HTTPS API, allowing you to mix Microsoft Fabric data with information from any other line-of-business application or service. They’re also where you put the business logic for any Fabric application.

Structuring apps to be coded by machines and managed by humans

Building with the Rayfin SDK produces a predictable application structure with YAML configurations and TypeScript code. That code can be tested locally against a Microsoft Fabric back end. When ready to be deployed and shared, it can be uploaded to Microsoft Fabric, which hosts both the Rayfin function code and any front-end assets. Apps will require Microsoft Fabric capacity to work, and can be accessed from inside the Microsoft Fabric portal, though users can access apps via published endpoints through their browsers.

Rayfin is one of the first examples of a new generation of SDKs and development tools that support both humans and agents. The human-readable Rayfin documentation is paired with an identical set of markdown files, ready for use by any agent working on Rayfin code. Microsoft’s partnership with Replit provides a coding agent to build Rayfin applications and a rendering canvas with familiar Power BI features, providing a place for non-coding business users to experiment with Fabric Apps using AI.

The strict structure Rayfin imposes on Fabric Apps aims to bridge the gap between AI-powered coding and traditional enterprise application development. By putting all the components of an application in a rigidly-defined environment, Rayfin helps ensure that AI-generated code remains secure, compliant, human readable, and human maintainable. Business users can quickly put together Fabric Apps with Rayfin and Replit. Then, if a Fabric App proves to have utility outside a narrow group of users, it can be transferred to the enterprise’s development team for further refinement and long-term operations.