28 debugging experiments reveal that AI struggles less with complexity than with missing information.

The post Bug Detection Blind Spots in AI Coding Harnesses (GStack and Beyond) appeared first on Towards Data Science.

Turning a demo agent into something that can keep real booking data

The post Building a Proper Backend for My LangGraph AI Agent appeared first on Towards Data Science.

Turning Codex from an interactive assistant into a programmable automation component

The post Running Codex as a Headless Agent appeared first on Towards Data Science.

Building the Responsible AI, security, and governance layers required for enterprise-ready agents

The post From Prototype to Production: The Architecture Behind Secure & Governed AI Agents appeared first on Towards Data Science.

5 principles that determine whether an agent system succeeds in production, explained through one I built for a $100M+ company.

The post Building Enterprise Agent Systems that People can Trust, Verify and Improve appeared first on Towards Data Science.

For years, web agents have worked one click at a time—and often fallen apart on long tasks. Microsoft Research’s Webwright makes a different bet: give the model a terminal and let it write the program instead. On long-horizon tasks, the same GPT-5.4 model jumps from 33.5% to 60.1% success. And instead of leaving behind a click trace, it leaves something you can actually use again: a command-line tool.

The post Webwright: Why AI Web Agents Should Write Code, Not Click appeared first on Towards Data Science.

How autonomous agents broke two decades of capacity planning — and what to build instead

The post Three Generations of Autoscaling — And Why Agentic Traffic Breaks All of Them appeared first on Towards Data Science.

Learn how to run OpenClaw bots for increased productivity

The post How to Orchestrate a Fleet of OpenClaw Bots appeared first on Towards Data Science.

A practical guide to choose the proper tool for your agentic workflows and systems

The post LangChain vs LangGraph: 4 Key Differences and When to Use Each appeared first on Towards Data Science.

I replayed the same 27 real production tasks through two local models, one hardware upgrade apart, to find out what it actually takes to replace Claude as the brain behind a 90-tool personal agent.

The post Can a Local LLM Run My AI Assistant? appeared first on Towards Data Science.

Building a production-ready web interface for a stateful LangGraph agent

The post Building a Streamlit UI for My LangGraph AI Agent appeared first on Towards Data Science.

A step-by-step guide to building a data agent and conversational interface that let business users to explore data in natural language without SQL

The post I Built an AI Data Agent Which Can Query Data and Answer Business Questions. Here’s How. appeared first on Towards Data Science.

A minimal loop with real API calls, validation, compact outputs, and trace evidence before adding an agent framework

The post I Built a Tool-Calling Agent in Python. Here’s How I Debugged It appeared first on Towards Data Science.

For decades, automation on the plant floor meant machines doing the same thing very fast, very reliably and very literally. A robotic arm welded exactly where it was told, a conveyor moved at a fixed speed, and a programmable controller executed rules written by an engineer months earlier. That model built the modern industrial economy. […]

Qualcomm has announced that it has completed its acquisition of Modular Inc, an innovator in AI-native software infrastructure. Modular’s software platform gives developers a unified way to optimize and deploy generative and agentic AI workloads across heterogenous computing systems. Combined with Qualcomm Technologies’ leadership in high-performance, energy-efficient compute, Modular strengthens the company’s ability to deliver […]

This blog article is part of a series on the Hiroshima AI Process (HAIP) Reporting Framework. The series explores different dimensions of the framework, its role in supporting trustworthy AI, and insights emerging from reports submitted by participating organisations.

In our first post on the AI Wonk, Aliki Foinikopoulou, our Head of Global Public Policy, discussed the rapid evolution of the AI landscape. Less than a year later, the emergence of new technologies and a rapidly evolving regulatory landscape have added new layers of complexity. Against this backdrop, trust is more critical than ever. AI presents a generational opportunity, but harnessing it responsibly requires governments to play an active, deliberate role in shaping its development and deployment.

Salesforce was pleased to be one of the first companies to contribute to the reporting framework developed by the OECD under the Hiroshima AI Process (HAIP), and we were pleased to have contributed to the second version of the reporting framework, which offers an expanded view of the AI value chain. Over the last year, enterprise agentic AI has fundamentally shifted the business landscape, transforming organisations from simply using AI to becoming agentic enterprises. Now, more than ever, a common framework and language to articulate a path to trusted AI are critical.

The governance gap we can’t afford to ignore

The most consequential shift in artificial intelligence is not happening in a research lab. It is happening right now through decisions in boardrooms, legislatures, and the daily choices of millions of people. Governance frameworks need to keep up.

These decisions and choices are more critical today because AI has expanded beyond large, generative models that respond to prompts into the era of agentic AI, where systems autonomously plan, execute and adapt across complex, multi-step workflows. These agents can browse the web, write and run code, negotiate, make purchases and decisions. All with real-world consequences.

The AI value chain has expanded dramatically, spanning model developers, cloud infrastructure providers, orchestration platforms, data brokers, enterprise deployers and end users. Each new layer introduces fresh challenges: accountability gaps, opaque decision-making, and the potential erosion of meaningful human control.

The question is no longer whether AI should be governed. The question is how governance frameworks can keep pace.

Fragmentation is the real threat

The primary hurdle to responsible AI adoption today is not only technical but also regulatory fragmentation. As nations race to establish frameworks to govern AI, we are seeing a patchwork of rules that sometimes take different approaches. The divergent definitions of risk and transparency across jurisdictions not only create compliance headaches; they also contribute to governance gaps that small businesses cannot afford to bridge, leaving the advantage solely with the largest incumbents.

Consider a mid-sized logistics company deploying AI agents to manage cross-border freight and customs compliance. Unlike large incumbents, it has no dedicated AI governance team. In a fragmented regulatory environment, demonstrating responsible AI use means navigating a different set of voluntary and mandatory rules in every market where it operates, an effective barrier to entry that has nothing to do with the quality or safety of its technology. A harmonised framework with clear, tiered requirements based on risk level rather than company size would change that calculus entirely, enabling broader participation in the responsible AI economy.

A global bank using AI agents to automate processes is another good example. The EU requires human-interpretable reasoning trails; US regulators focus on disparate impact testing; the Monetary Authority of Singapore (MAS) applies its own Fairness, Ethics, Accountability, and Transparency (FEAT) principles. Without a common standard, the bank either builds to the most restrictive version globally, which is expensive, or limits deployment to certain markets, leaving genuine value unrealised.

These aren’t hypothetical edge cases. They are the daily reality for Salesforce customers operating across jurisdictions.

We have seen this dynamic before. In the early days of the internet, cybersecurity was a fragmented landscape of jurisdiction-specific standards. It was not until we moved toward global interoperability through frameworks such as NIST and ISO that businesses could demonstrate their security programmes at scale. AI governance must follow the same blueprint to avoid fragmentation as the final reality.

Why HAIP matters

At Salesforce, my team in the Office of Ethical and Humane Use guides the responsible design, development, and deployment of our technologies. Ethical considerations must be embedded from the start in the product design of the AI platform and the agents themselves, and not just because of regulations, but because it is what our customers expect from Salesforce. But internal commitment alone is not enough. It must be paired with strong, globally coherent governance.

That’s why we are pleased to once again participate in the HAIP reporting framework and in the process to both streamline and broaden its base of participation. The ubiquity of AI means that frameworks like this need to be accessible and relevant to companies of all sizes across markets worldwide.

HAIP matters for several reasons, including:

• A common language: The HAIP Reporting Framework provides a standardised baseline that allows regulators and companies to compare compliance processes across the industry, an essential in a world where agentic AI means different things to different people. For a company like Salesforce, whose Agentforce platform is deployed by enterprises across the EU, US, Japan, and beyond, this matters enormously. Today, a multinational client must configure separate compliance documentation for each jurisdiction, even when the underlying agent and its safeguards are identical. A common language means that responsible design, built once, can be recognised everywhere.
  
  
• Global interoperability: In a fragmented regulatory environment, HAIP acts as a diplomatic bridge, ensuring that a company’s safety commitments in San Francisco carry weight in Tokyo and Brussels. Salesforce voluntarily publishes its responsible AI practices and invests heavily in making them robust, but without interoperability, those commitments must be re-translated for every regulatory context. That is not a problem of intent; it is a problem of infrastructure. HAIP provides that infrastructure.
  
  
• From “trust me” to “show me”: By making reports public on the OECD.AI platform, HAIP transforms transparency from a compliance burden to a competitive advantage and inspires a race to the top. Companies currently publish reports, such as Salesforce’s Trusted AI and Agents Report, on a voluntary basis. Centralising this kind of information from across industries on a single platform can further bolster the industry by publicly demonstrating a strong commitment to responsible AI. Critically, it also lowers the barrier for smaller companies: a streamlined, interoperable voluntary framework means that responsible AI is no longer a signal only large incumbents can afford to send.

A blueprint worth protecting

Just as the internet required global standards to scale safely, so too does AI. Just as the Payment Card Industry Data Security Standard created a single payment security standard that allowed small businesses to accept credit cards globally without a compliance army, a coherent AI governance framework provides companies of all sizes with a clear, achievable bar rather than an ever-shifting patchwork of national rules. The Hiroshima AI Process is that tool, a shared blueprint for trust that governments and the private sector must now work together to strengthen and expand.

Realising the opportunity of agentic AI responsibly demands more than good intentions. It demands frameworks that are globally coherent, publicly accountable, and built to keep pace with the technology itself. Salesforce remains committed to that work because the future of AI should be governed by shared principles that are ethical, agile, humane, and built to last.

The post HAIP is transforming transparency from a compliance burden to a competitive advantage appeared first on OECD.AI.

AI agents and agentic AI based on large language models are becoming more autonomous and capable of interacting with both physical and virtual environments. As the capabilities of these AI systems grow, they are gaining visibility, and with reason. It is reaching a point where they could become the driving force behind innovation, investment and improved productivity across sectors by streamlining processes and enabling more efficient operations.

While ideas related to agency have long been explored in academic research in fields such as philosophy, economics and computer science, recent advances in AI are stretching conceptual boundaries. As AI’s capabilities evolve, so do our shared understanding of what qualifies as AI agent and agentic AI.

The OECD report, The agentic AI landscape and its conceptual foundations, developed by the OECD.AI Expert Group on Agentic AI, helps clarify what AI agents and agentic AI are and how they differ. Grounded in the OECD AI system definition, the analysis examines how these terms are defined and used across the literature. By analysing key features, overlaps and distinctions and mapping them to the core elements of the OECD definition of an AI system, the report helps to establish more precise and consistent terminology. And in a rapidly evolving field, conceptual precision is essential for effective, well-informed governance.

Three key messages stand out in the report:

• AI agents and agentic AI are closely related, but not interchangeable.
• Agentic AI ought to be seen as a socio-technical paradigm.
• Despite technological gaps and varying levels of maturity in areas such as digital security and privacy, uptake is growing.

The common foundations and meaningful distinctions of AI agents and agentic AI

Our analysis shows that AI agents and agentic AI share foundational characteristics. Both involve systems with a degree of autonomy that pursue goals and can perceive and act within physical and virtual environments.

However, there are differences that mean these terms are not interchangeable.

1. AI agents can be understood as systems that perceive and act on their environment with a degree of autonomy, using tools as needed to achieve specific goals and adapt to changing inputs and contexts.
2. By contrast, agentic AI generally refers to systems composed of multiple co-ordinated AI agents that can break down tasks, collaborate and pursue complex objectives autonomously over extended periods. Agentic AI systems are designed to operate in more open-ended, less predictable physical and virtual environments, and to function with minimal human supervision.

In short, agentic AI is more complex, as it can co-ordinate multiple agents, perform task decomposition and delegation, and sustain operations over longer periods. It can also operate in more complex, less predictable environments with limited human oversight.

Agentic AI as a socio-technical paradigm

Agentic AI systems are not isolated technical artefacts. They are frequently embedded in social contexts and interactions and operate within a socio-technical paradigm.

Their value lies not only in autonomous action, but in interaction with other AI agents, humans and institutional processes. Co-ordination and negotiation across these actors require advanced reasoning capabilities, robust infrastructure and reliable communication protocols.

This relational perspective is an essential part of what agentic AI is. This means that understanding how they interact within broader ecosystems is essential to designing agentic AI systems that function responsibly and effectively, particularly in open or high-stakes environments.

Uptake is accelerating, but maturity is uneven

The report also presents descriptive evidence on trends in AI agent adoption. Many developers have already integrated them into their toolkits, and survey data indicate that nearly half of respondents on Stack Overflow use them or plan to do so.

To be clear, adoption should not be confused with maturity. Developers highlight opportunities to further strengthen the security, privacy and accuracy of AI agents. These concerns underscore an important point: as the capabilities of agentic AI advance rapidly, progress in robust, trustworthy AI systems must keep pace.

A foundation for further analysis

Overall, the report provides a descriptive overview of the agentic AI landscape, clarifying key concepts and characteristics and establishing a shared analytical foundation. By anchoring the discussion in the OECD AI system definition, it aims to promote coherence across technical and policy communities.

Looking ahead, an improved understanding of real-world use will be essential to identify where safeguards, standards, and governance mechanisms will be most effective. Policy-relevant typologies that build upon this work could help guide governance efforts to distinguish systems by level of autonomy, degree of adaptiveness, domain of operation and scale of impact. Evidence-based policymaking will require more empirical data on how AI agents and agentic AI are being adopted and used across sectors, as well as clearer evidence of their broader implications and impacts.

This report contributes to a clearer, shared understanding of agentic AI and provides a basis for thoughtful, forward-looking policy grounded in conceptual clarity. As agentic AI systems become more capable of coordinating multiple AI agents, taking action and operating over longer periods, governance conversations have to keep pace.

The post Can we create a clear understanding of what agentic AI is and does? appeared first on OECD.AI.