Ready Tensor, Inc. @ready_tensor

In agentic AI, failures don’t usually look like traditional system errors (e.g., a 500 error). Instead, they’re silent: the system keeps running but starts behaving in ways that quietly erode trust. These subtle failures are harder to catch, but critical to address. Common…

Monitoring vs. Observability in Agentic Systems Monitoring tracks system metrics like latency, error rates, and resource usage. It alerts you when thresholds are crossed. Think of it as a fire alarm, it tells you something is wrong but not why. Observability provides the…

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Giskard is an open-source AI testing framework that serves as your AI’s continuous quality monitor. It evaluates conversational agents before and after deployment to ensure they stay safe, reliable, and trustworthy. With Giskard, you get automated vulnerability detection,…

Agentic AI systems are powerful but face risks like prompt injection, data leaks, and biased outputs. Guardrails, a Python framework, addresses this by validating inputs and outputs at runtime, acting as a watchtower to keep LLM systems safe, reliable, and aligned. How It…

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We launched a new series spotlighting standout publications from our platform. Engaging Reads brings together works that are approachable, thought-provoking, and designed to spark ideas for students, researchers, and practitioners alike.Being featured in Engaging Reads serves as…

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The shift from scripted execution to autonomous decision-making in pursuit of goals is what makes Agentic AI powerful. At its simplest, Agentic AI can be thought of as the autonomy engine for AI systems , the design principles and methods that allow AI to go beyond static scripts…

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Agentic systems are made up of two layers: Architectural components - the foundational building blocks. Behavioral components - the design patterns that give them intelligence and reliability. Architectural components These form the system’s core “hardware”:…

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If you’ve been wondering how to bring AI agents into your workflows, here’s the good news: industries like healthcare, finance, logistics, and education are already doing it. Unlike static automation, Agentic AI systems feel less like rigid software and more like collaborative…

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Important Update on ReadyTensor's Agentic AI Developer Certification Program The program remains completely free, and you can  join at any time. All you need to do is start taking the lectures and submit your projects before the stipulated deadlines to have them reviewed and…

OpenAI has an Agents SDK that lets you build autonomous agents and even multi-agent systems , going beyond the usual single prompt/response setup. The core OpenAI SDK is great when you just need direct model calls like chat completions, audio transcription, embeddings, or image…

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API Architectural Styles

🎉 Exciting News! Certifications for the Agentic AI Developer Program have been rolled out! 🚀 Learners who consistently completed all three modules, submitted their projects on time, and actively collaborated with others have now received their certificates. Congratulations to…

Introducing ‘Engaging reads’ on ReadyTensor We’re excited to launch a new series spotlighting standout publications from our platform. Engaging Reads brings together works that are approachable, thought-provoking, and designed to spark ideas for students, researchers, and…

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Contribution to ReadyTensor’s Agentic AI developer program is still open. The program has already grown to over 6,000 developers and counting, and we’re welcoming community members to contribute articles, tutorials, and technical explainers. Our goal is to make this the most…

Benchmark scores and leaderboards can be useful, but they’re also easy to misread. A top-ranked model isn’t always the best for your needs , some models excel in academic benchmarks but feel unnatural in real use, while others perform better in human preference tests even if they…

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Building great agentic systems isn’t about flashy diagrams or using as many agents as possible, it’s about solving the problem cleanly, reliably, and with minimal complexity. Here are some key takeaways when designing AI agents: -Start with the Work, Not the Workers : Don’t…

MCPs allow AI agents to securely and seamlessly interact with external tools and data. Before MCP, AI integrations were a nightmare: every capability needed a custom integration, each with its own security model and brittle code. MCP changes this by providing: Universal…

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In AI systems, especially those powered by LLMs and agentic workflows, testing is different because outputs vary. Validation must go beyond accuracy to include structure, behavior, tone, safety, and relevance. Security: Protects systems from malicious users, attacks, or…

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Agentic systems can cause harm beyond just bugs or exploits , they can produce toxic, biased, or misleading outputs. That’s why AI safety and alignment are essential to recognize risks and design systems that behave responsibly. Most failures don’t come from malicious actors.…

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