Ample Insight

How AI Agents Chain Tools, Memory, and Reasoning? 1. Reasoning: This is the brain of the AI agent. It receives the user’s goal as a query and uses its reasoning agent (using frameworks like ReAct) to break the goal into a series of smaller, logical steps. 2. Tools: For each step, the agent selects an appropriate tool. These can be external programs or APIs that it can call upon, such as a web search, a calculator, a code interpreter, or documents. 3. Memory: This is like the notebook. The agent records the outcome of each tool’s use in its memory. This notebook allows it to learn from results, maintain context over long conversations, and refine its plan based on new findings. This continuous loop of reasoning, acting with a tool, and remembering the outcomes creates a chain. Finally, when the agent is satisfied that it has fulfilled the user’s query, it responds to the user. Over to you: Have you used AI agents? -- We just launched the all-in-one tech interview prep platform, covering coding, system design, OOD, and machine learning. Launch sale: 50% off. Check it out: https://bit.ly/bbg-yt #systemdesign #coding #interviewtips .

In my role as Chair of the International AI Safety Report, an effort backed by over 30 countries and international organisations including the European Union, OECD - OCDE and United Nations, I work with 100 researchers to help policymakers understand the capabilities and risks of general-purpose AI. The field is clearly changing far too quickly for a single annual report to suffice. That’s why today we’re introducing Key Updates: shorter, focused reports on critical developments in AI that will be published between editions of the full report. Our first Key Update focuses on advancements in AI capabilities, and what they mean for AI safety. You can read it here: https://lnkd.in/eKVGF7dy Some of the key findings it covers include: ➡️ Impressive performance improvements. Several AI systems can now solve International Mathematical Olympiad problems at gold medal level and complete a majority of problems in several databases of real-world software engineering tasks. ➡️ The rise of “reasoning” models. Recent gains have come mainly from training and deployment techniques that allow AI models to generate interim steps before producing final answers. This demonstrates that AI capabilities can advance significantly through post-training techniques and additional computing power at inference time, not just through scaling model size. ➡️ Some signals of real-world adoption. In a recent StackOverflow survey, a majority of software developers report using AI tools daily to help design experiments, process data, and write reports. Yet we still don’t know much about AI use in many other domains, nor crucially about how AI use affects productivity overall. ➡️ Stronger safeguards from developers. Leading AI developers recently activated enhanced protections on their most capable models as a precautionary measure, given possibilities like misuse to build weapons. ➡️ Emerging oversight challenges. AI models increasingly demonstrate an ability to distinguish evaluation tasks from real-world tasks, possibly complicating our ability to reliably test their capabilities before deployment. These developments raise further questions about control, monitoring, and governance as AI systems become more capable.

Artificial Intelligence (AI) is no longer “emerging” — it’s here, shaping the way we work, compete, and innovate. Yet, Canadian businesses are adopting AI at rates below OECD averages, holding back productivity and growth. How can we close that gap? Join us for an engaging virtual discussion on September 11, 2025, 2:00–4:00 PM ET on Increasing Business AI Adoption Through Education Featuring insights from A.J. Bhadelia (Cohere), Garry Ma (Ample Insight), and Bill Syrros (BDO Canada), the session will explore: ✅ AI fundamentals & common misconceptions ✅ Real-world applications & ROI for Canadian businesses ✅ Practical steps to identify and scale AI opportunities Moderated by Ulrike Bahr-Gedalia, Senior Director, Digital Economy, Technology & Innovation, Canadian Chamber of Commerce. 👉 Register here: https://hubs.ly/Q03Gc2bZ0

Gen AI only delivers value when people trust it, and when leaders empower teams to shape the journey. High performers are already proving that trust, culture, and a clear North Star are the true differentiators. The message for CEOs is clear: bring people along, and transformation will follow. How is your organization preparing? https://mck.co/4fFNXDs

🚨 New paper alert! 💡 Reducing MRI scan times can improve patient care and lower healthcare costs 🚀 The paper below introduces the Trust-Guided Variational Network (TGVN), a deep learning framework that reconstructs high-quality MR images from heavily under-sampled k-space data, by integrating side information, like incomplete or noisy images from a different contrast! 📉 The result: significant scan time reduction without hallucinated features or diagnostic loss — even at aggressive acceleration factors. TGVN is robust across contrasts, anatomies, and field strengths — opening the door to smarter, faster, and more accessible MRI. Read the paper: 🔗 https://lnkd.in/e94DkZ-4 Authors: Arda Atalik, Sumit Chopra, and Dan Sodickson

The Agentic AI Learning Roadmap 1 - An AI Agent is a system capable of autonomous actions, reacting to its environment, using tools (APIs, Internet, code, etc), and can work under human guidance. 2 - To build AI agents, one must know tools like Python, Jupyter, PyTorch, and GitHub Copilot. These enable coding, experimentation, and integration with AI libraries and APIs. 3 - GenAI Foundational Models Familiarity with large models like GPT, Gemini, LLaMa, DeepSeek, and Claude is essential. These models provide the base intelligence that agents can use for reasoning, generation, and understanding. 4 - AI Agent Development Stack Tools like Langchain, AutoGen, Crew AI, and frameworks like Semantic Kernel and Hugging Face power agent workflows. These components manage tasks, memory, and external tool integrations in agent pipelines. 5 - API Design Understanding API design approaches like REST, GraphQL, gRPC, and SOAP is crucial to building interoperable agents. Key concepts include HTTP methods, status codes, versioning, cookies, headers, and caching. 6 - Type of AI Agents Learn about the several types of AI agents, such as simple reflex, model-based reflex, goal-based, utility-based, and learning agents. Each varies in complexity. 7 - AI Agent System Architecture AI agents can operate as single agents, in multi-agent systems, or in human-machine collaboration. Architecture depends on the use case. Over to you: What else will you add to the AI Agent Learning Roadmap? -- We just launched the all-in-one tech interview prep platform, covering coding, system design, OOD, and machine learning. Launch sale: 50% off. Check it out: https://bytebytego.com #systemdesign #coding #interviewtips .