# autonomous-agents Published articles for autonomous-agents. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Agent Anomaly Detection, now in Private Preview on the Gemini Enterprise Agent Platform DevFeed: [Agent Anomaly Detection, now in Private Preview on the Gemini Enterprise Agent Platform]() Original publisher: [Read original article]() Author: Achuth Narayan Rajagopal Published: 2026-09-17T01:25:27.608736Z Content type: release Language: en Sources: [Google Developers Blog]() Topics: [agent observability](), [OpenTelemetry](), [Security](), [Traces](), [API]() Tags: [agent](), [anomaly-detection](), [api](), [autonomous-agents](), [gemini](), [opentelemetry](), [security]() ### AI overview Google announces Agent Anomaly Detection in private preview on the Gemini Enterprise Agent Platform. The feature analyzes agents' reasoning traces, tool calls, logs, and execution flows to identify behavioral anomalies, suspicious intent, and policy violations. ### Source excerpt Agent Anomaly Detection is a new, out-of-band oversight layer for the Gemini Enterprise Agent Platform that analyzes OpenTelemetry traces and tool calls to catch behavioral risks without adding runtime latency to live requests. It utilizes a multi-tiered detection pipeline--combining lightweight statistical scanning with deep LLM-based reasoning--to identify logical anomalies and policy violations grounded in the OWASP Agentic Top 10. Developers can triage these automated findings within Security Command Center or leverage the exposed API to programmatically block subsequent tool calls when an agent breaches defined risk thresholds. ## Fragments: September 1 DevFeed: [Fragments: September 1]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-09-01T19:50:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [AI Bots](), [AI Chat]() Tags: [agents](), [ai](), [architecture](), [autonomous](), [autonomous-agents](), [benchmark](), [ci](), [claude](), [evaluation](), [gpu](), [kernel](), [llm](), [mcp](), [memory](), [nvidia](), [optimization](), [reasoning]() ### AI overview A fragment-style developer roundup covers concerns about detecting AI-generated prose, a long-horizon autonomous-agent architecture used for GPU kernel optimization and reasoning benchmarks, a brief MCP comparison, and the effect of AI agents on CI workflows. ### Source excerpt Like many readers, I'm wary of AI generated prose. Simon Wilison has written an LLM cliché highlighter - paste in some text, or a URL, and it will flag various patterns common to LLMs. It references a wikipedia page of signs of AI writing. That page points out that: Humans are notoriously bad at distinguishing human and LLM-generated text. While research on humans' abilities to detect AI-generated text is still limited, a 2025 study has shown that human ability to distinguish LLM text from human is no better than random chance. Another 2025 study on German theses has shown that humans managed a "recognition rate of 57% for AI texts and 64% for human-generated texts".[ Not just do I find myself repelled by prose with an LLM-voice, I also wonder how accurate my reaction is. I'm old enough to see all sorts of new tic-phrases appear, and in the past would just chalk it up to youngsters or airport business books. (Not to mention Americanisms, which I'll get used to momentarily.) ❄ ❄ ❄ ❄ ❄ NVIDIA's technical blog reports on an Architecture for Long-Horizon Autonomous Agents. Their research group used a combination of Claude Opus 5 and a harness called AVO, and used it first to do GPU kernel optimization and then a broader reasoning benchmark (ARC-AGI-3). Both of these were long-term tasks, for the kernel optimization the agent ran for seven days. AVO is designed to preserve progress beyond a single model context. Two mechanisms are particularly important: persistent memory and supervision. Persistent memory carries forward prior implementations, evaluation results, compiler and profiler outputs, and accumulated reasoning, allowing the agent to resume from the current state rather than repeatedly reconstructing the search. The supervisor monitors the broader trajectory for stagnation or repeated unproductive cycles and can redirect the main agent toward alternative strategies when needed. During the seven-day attention-kernel run, the main agent remained responsible for de ## NVIDIA AVO Reaches 100% on ARC-AGI-3, Demonstrating a Frontier-Level General-Purpose Architecture for Long-Horizon Autonomous Agents DevFeed: [NVIDIA AVO Reaches 100% on ARC-AGI-3, Demonstrating a Frontier-Level General-Purpose Architecture for Long-Horizon Autonomous Agents]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-21T13:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Bots]() Tags: [agentic](), [agentic-ai-generative-ai](), [agents](), [ai-agent](), [architecture](), [autonomous](), [autonomous-agents](), [benchmark](), [coding](), [gpu](), [kernel](), [language-models](), [nvidia](), [nvidia-research](), [optimization](), [performance](), [software-engineering](), [top-stories](), [trustworthy-ai-cybersecurity]() ### AI overview NVIDIA introduces AVO, a general-purpose coding-agent architecture intended for sustained autonomous work on long, multistep tasks. The article describes its use in GPU-kernel optimization and its adaptation to the ARC-AGI-3 benchmark through different task-specific tools and evaluation. ### Source excerpt A frontier language model is only one component of an AI agent. The surrounding agent system--often called a harness--determines how the model receives... ## How AgentFlo built AI sales agents with Amazon Bedrock AgentCore - Part 2 DevFeed: [How AgentFlo built AI sales agents with Amazon Bedrock AgentCore - Part 2]() Original publisher: [Read original article]() Author: Muhammad Musab Iqbal Published: 2026-08-21T10:01:18Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [AI Bots](), [ai observability]() Tags: [advanced-300](), [agents](), [ai](), [ai-agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [architecture](), [autonomous](), [autonomous-agents](), [aws](), [aws-fargate](), [customer-solutions](), [observability](), [policy](), [sales](), [security](), [serverless]() ### AI overview AgentFlo's second architecture post explains how it uses Amazon Bedrock AgentCore and AWS serverless components to operate AI sales agents with layered safeguards. It emphasizes controls before requests, during tool execution, and after responses, while reporting an early 12% net-revenue uplift. ### Source excerpt Part 2: how AgentFlo built trusted, reliable AI sales agents on Amazon Bedrock AgentCore and AWS serverless architecture. Learn the three-layer guardrails, grounded data foundation, and end-to-end observability behind a +12% net revenue uplift, plus what's next for real-time voice and server-side tool execution. ## Give a scheduled agent access to a user's Google, Slack, and Jira with no signed-in session DevFeed: [Give a scheduled agent access to a user's Google, Slack, and Jira with no signed-in session]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-12T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [Tutorial](), [API keys](), [Authorization](), [OAuth](), [Slack](), [Google]() Tags: [agent](), [api](), [audit](), [auth0](), [authorization](), [autonomous-agents](), [browser](), [google](), [jira](), [logging](), [rotation](), [scheduled](), [scopes](), [slack](), [token](), [tokens](), [tutorial]() ### AI overview A tutorial showing how to give scheduled workers access to a user's Google, Slack, and Jira through WorkOS Pipes without a signed-in session. It covers scoped credential retrieval, token relay, rotation, revocation, and audit logging. ### Source excerpt A runnable tutorial for pulling per-connection third-party credentials from WorkOS Pipes with nobody logged in, plus relay, rotation, revocation, and audit logging. ## CTO.new Coordinates Tens of Thousands of AI Agents with a Database per Project on Turso DevFeed: [CTO.new Coordinates Tens of Thousands of AI Agents with a Database per Project on Turso]() Original publisher: [Read original article]() Author: Glauber Costa Published: 2026-08-06T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [AI Bots](), [AI-assisted coding](), [AI, ML & Data Engineering]() Tags: [ai-agents](), [autonomous-agents](), [coding](), [database](), [infrastructure](), [scalability](), [security](), [software-architecture](), [turso](), [vibe-coding]() ### AI overview CTO.new uses a dedicated Turso database for each AI-agent project to coordinate workloads at scale. The article describes lower database costs compared with its earlier Supabase setup and the platform's return to Turso for agent infrastructure management. ### Source excerpt CTO.new gives every team of AI agents its own Turso database as a coordination layer, running tens of thousands of databases on a $500 per month plan. ## Evo Continuous Offensive Security Is Here Pentesting Grade Coverage For The 350 Days A Year You Aren't Testing DevFeed: [Evo Continuous Offensive Security Is Here Pentesting Grade Coverage For The 350 Days A Year You Aren't Testing]() Original publisher: [Read original article]() Author: John Carione Published: 2026-08-04T04:00:00Z Content type: release Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Responsibility & Safety](), [AI-assisted coding]() Tags: [agentic](), [agents](), [ai](), [ai-coding](), [ai-security](), [application-security](), [aspm](), [attacks](), [autonomous](), [autonomous-agents](), [availability](), [black-hat](), [blog](), [cos](), [cybersecurity](), [developer](), [developers](), [development](), [devops](), [interest](), [security](), [snyk-platform](), [testing]() ### AI overview Snyk announces general availability of Evo Continuous Offensive Security, an autonomous, AI-powered pentesting offering intended to validate exploitable flaws between traditional pentests. The article frames the launch as part of an expanded AI security platform for AI-accelerated development. ### Source excerpt Snyk Evo Continuous Offensive Security brings autonomous, AI-powered pentesting to the 350 days between traditional tests, uncovering exploitable flaws attackers can find first. ## Orchard: An open framework for scalable agentic AI DevFeed: [Orchard: An open framework for scalable agentic AI]() Original publisher: [Read original article]() Author: Baolin Peng, Wenlin Yao, Qianhui Wu, Hao Cheng, Jianfeng Gao Published: 2026-08-03T16:00:00Z Content type: article Language: en Sources: [Microsoft Research]() Topics: [Artificial Intelligence](), [AI Research](), [Open Source](), [Frameworks](), [Kubernetes](), [Reinforcement learning](), [computer-use](), [codex](), [OpenClaw]() Tags: [agentic](), [agentic-ai](), [agents](), [ai](), [artificial-intelligence](), [autonomous](), [autonomous-agents](), [building](), [codex](), [evaluation](), [framework](), [infrastructure](), [kubernetes](), [learning](), [models](), [open](), [open-source](), [research](), [research-blog](), [train](), [workflows]() ### AI overview Orchard is an open-source framework for training and evaluating agentic AI systems across software engineering, web navigation, and personal-assistant tasks. Its reusable Orchard Env provides Kubernetes-based infrastructure for data collection, reinforcement-learning rollouts, and evaluation, while Orchard-SWE, Orchard-GUI, and Orchard-Claw demonstrate strong results from relatively small open-weight models. ### Source excerpt Orchard is an open-source framework for the research community to train and evaluate AI agents across task types. It reduces complexity while supporting strong performance from smaller models by enabling researchers to reuse the same infrastructure. The post Orchard: An open framework for scalable agentic AI appeared first on Microsoft Research. ## Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence DevFeed: [Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence]() Original publisher: [Read original article]() Published: 2026-07-30T20:36:36Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Google](), [AI-generated research reports](), [Large language models (LLMs)](), [Hallucination detection](), [LLM evaluation / benchmarking]() Tags: [agents](), [ai](), [autonomous-agents](), [benchmarks](), [evaluation](), [general-science](), [google](), [hallucinations](), [language-models](), [machine-intelligence](), [natural-language-processing](), [research](), [research-prototype]() ### AI overview Google Research introduces the Science One Framework, an experimental autonomous research prototype built around Chain-of-Evidence. It is designed to make AI-generated research verifiable by linking claims to supporting evidence and by auditing papers against their code and evidence. The article reports that the framework eliminates phantom references and produces fully verifiable scores in the described evaluations. ### Source excerpt General Science ## No Dumb Questions: What is the AI bottleneck? How does context engineering fix it? DevFeed: [No Dumb Questions: What is the AI bottleneck? How does context engineering fix it?]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-07-24T16:00:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [Artificial Intelligence](), [AI Strategy](), [Data Science]() Tags: [agentic-ai](), [ai](), [ai-adoption](), [autonomous-agents](), [data-science](), [no-dumb-questions]() ### AI overview Stack Overflow's No Dumb Questions series explores an AI adoption bottleneck: AI tools can perform tasks but often lack the surrounding context from email threads, Slack conversations, and meetings. Michael Foree explains how context engineering can help people provide relevant information so AI produces more useful responses. ### Source excerpt In this No Dumb Questions, Stack's Director of Data Science Michael Foree teaches Phoebe about AI context, context engineering, and what she can do to become a better context engineer. ## Deploy agents you can trust with centralized AI governance DevFeed: [Deploy agents you can trust with centralized AI governance]() Original publisher: [Read original article]() Author: Kristin Crosier Published: 2026-07-21T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [ai-governance](), [Responsibility & Safety](), [audit trail](), [SIEM, Security, Observability](), [Model Context Protocol](), [observability]() Tags: [agentic](), [ai](), [ai-governance](), [audit-trail](), [autonomous](), [autonomous-agents](), [enterprise](), [hosting](), [infrastructure](), [mcp](), [observability](), [thought-leadership](), [vibe-coding]() ### AI overview This article explains why enterprises struggle to deploy autonomous agentic systems: organizations lack confidence in agents that can access data and tools without unique identities, audit trails, or kill switches. It argues for a centralized AI governance layer enforced by infrastructure that agents cannot access or modify, positioned between agents and the data, tools, identities, and models they use. The proposed governance platform also supports scaling through AI gateways, MCP hosting, observability, and budgeting. ### Source excerpt Discover why organizations are struggling to deploy and scale agentic systems, and how a centralized AI governance platform can help you trust and scale agents. ## How to Govern Autonomous Agents in Enterprise AI Factories DevFeed: [How to Govern Autonomous Agents in Enterprise AI Factories]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-06-29T15:50:13Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [AI Factory](), [systems](), [vulnerability management](), [configuration](), [Network](), [SOC](), [ide](), [browser](), [Terminal]() Tags: [agentic-ai-generative-ai](), [agents](), [ai](), [ai-agent](), [ai-factory](), [architecture](), [autonomous](), [autonomous-agents](), [browser](), [data-center-cloud](), [enterprise](), [featured](), [ide](), [mlops](), [network](), [openshell](), [policy](), [soc](), [telemetry](), [vulnerability-management]() ### AI overview This tutorial presents NVIDIA's Secure Agent Workspace Reference Design for governing autonomous AI agents in enterprise environments. It explains how to separate the presentation layer from managed agent execution and enforce identity, network access, credentials, runtime policy, auditing, human review, and workspace isolation. ### Source excerpt AI agents are quickly moving beyond chat. They inspect code, run tests, read documents, search knowledge bases, query internal systems, and operate for hours on... ## Agentrification and the Agentrification Index DevFeed: [Agentrification and the Agentrification Index]() Original publisher: [Read original article]() Author: Graham Odds Published: 2026-06-29T00:00:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [digital]() Tags: [agentic-ai](), [ai](), [ai-agents](), [artificial-intelligence](), [autonomous](), [autonomous-agents]() ### AI overview This satirical post defines "Agentrification" as the displacement of human character and participation in spaces, platforms, and professions by autonomous AI agents. It introduces the Agentrification Index (AIx) as a diagnostic tool for quantifying this structural decay. ### Source excerpt This satirical post defines Agentrification as the sociotechnical phenomenon in which the original character of a human-centric space, platform, or profession is systematically displaced, optimised, or rendered obsolete by the arrival of autonomous AI agents, i.e. digital gentrification. It introduces the Agentrification Index (AIx) as a diagnostic tool to quantify this structural decay. ## Designing a team of agents DevFeed: [Designing a team of agents]() Original publisher: [Read original article]() Author: Nicolas Fränkel Published: 2026-05-03T00:00:00Z Content type: opinion Language: en Sources: [Nicolas Fränkel]() Topics: [Artificial Intelligence](), [Claude Code](), [Software Engineering](), [Code]() Tags: [agentic-engineering](), [agents](), [ai](), [autonomous-agents](), [claude-code](), [software-engineering](), [technical]() ### AI overview The author describes experimenting with AI for software engineering by designing a team of autonomous agents to implement features from design through implementation. The article discusses coding assistants, agentic engineering levels, Claude Code's experimental Agent Teams feature, and subagents. ### Source excerpt I continue to experiment with AI in the context of software engineering. I'm fortunate that my team supports me in exploring different ways to improve our daily work. This week, I designed a team of autonomous agents to implement features, from design to implementation. Why autonomous agents? A long time ago, we were delighted when the IDE offered auto-completion. In the previous two years, things have changed. A lot. Coding assistants have become our primary interfaces for coding. ## How to Negotiate Usage-Based Billing for AI Agent Products DevFeed: [How to Negotiate Usage-Based Billing for AI Agent Products]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-22T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [AI Agent](), [Software as a service]() Tags: [agent](), [ai](), [ai-agent](), [autonomous-agents](), [enterprise](), [guide](), [how-to](), [pricing](), [usage-based-billing]() ### AI overview A vendor-side guide to negotiating usage-based billing contracts for AI agent products. It explains how to define usage units, structure spending floors and caps, set overage rates and true-up schedules, and use contract clauses that balance buyer predictability with variable infrastructure costs. ### Source excerpt Vendor-side guide to negotiating usage-based billing contracts for AI agent products. Covers unit of measure, floor and cap structures, overage rates, true-up cadence, and sample term-sheet clauses. ## How Nx Uses Monorepos and Self-Healing CI to Support Continuous AI Agent Sessions DevFeed: [How Nx Uses Monorepos and Self-Healing CI to Support Continuous AI Agent Sessions]() Original publisher: [Read original article]() Author: Victor Savkin Published: 2026-01-21T00:00:00Z Content type: opinion Language: en Sources: [Juri Strumpflohner]() Topics: [Artificial Intelligence](), [Loop Engineering](), [ci](), [Orchestration](), [coding](), [Claude Code]() Tags: [agents](), [ai](), [ai-agents](), [autonomous](), [autonomous-agents](), [ci](), [claude-code](), [coding](), [orchestration](), [scale]() ### AI overview The article argues that fragmented repositories and separate local and CI environments interrupt autonomous AI agent work. It presents Nx monorepos and Self-Healing CI as ways to let agents maintain context, update multiple projects, and iterate on CI with fewer human handoffs. ### Source excerpt As AI agents become more autonomous, writing code is no longer the constraint. The organizations that win will be the ones where more work can be delegated to agents running uninterrupted. ## Introducing: Devstral 2 and Mistral Vibe CLI. DevFeed: [Introducing: Devstral 2 and Mistral Vibe CLI.]() Original publisher: [Read original article]() Published: 2025-12-09T12:00:00Z Content type: news Language: en Sources: [Mistral AI Blog]() Topics: [Artificial Intelligence](), [coding](), [Large Language Model](), [Open Source](), [Automation](), [GitHub Copilot CLI](), [Transformer](), [Command-line interface](), [context window](), [Terminal](), [Fine-tuning](), [on-prem]() Tags: [ai](), [ai-assistants](), [automation](), [autonomous-agents](), [claude](), [cli](), [code](), [coding](), [context-window](), [deepseek](), [fine-tuning](), [hardware](), [models](), [on-prem](), [open-source](), [terminal]() ### AI overview Mistral introduces Devstral 2, Devstral Small 2, and the Mistral Vibe CLI. The open coding models target autonomous software engineering, with deployment options spanning APIs, local consumer hardware, and on-premises environments. Devstral 2 supports a 256K context window, codebase-wide changes, failure recovery, and fine-tuning, while Mistral Vibe provides terminal-based code automation. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## The Agentic OODA Loop: How AI and Humans Learn to Defend Together DevFeed: [The Agentic OODA Loop: How AI and Humans Learn to Defend Together]() Original publisher: [Read original article]() Author: Manoj Nair Published: 2025-11-10T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Securing AI](), [ai security](), [Security](), [Artificial Intelligence]() Tags: [adaptive](), [agentic](), [agentic-security](), [agents](), [ai](), [autonomous](), [autonomous-agents](), [awareness](), [blog](), [collaboration](), [context](), [cycles](), [executive](), [false-positive](), [loops](), [model](), [pmm](), [policy](), [real-time](), [reasoning](), [security](), [systems](), [visibility]() ### AI overview This article presents the Agentic OODA Loop as a model for human and AI security engineers to defend AI-native systems. Inspired by the fighter-pilot cycle of Observe, Orient, Decide, and Act, it advocates adaptive defense that gains real-time visibility, reasons about context, automates policy enforcement and remediation with human oversight, and continuously learns from alerts, false positives, and exploit attempts. ### Source excerpt Discover how AI and human security engineers collaborate to defend against evolving threats at machine speed. Learn about the new mindset for adaptive, intelligent, and symbiotic defense in the age of Agentic AI. ## Introducing Mistral AI Studio. DevFeed: [Introducing Mistral AI Studio.]() Original publisher: [Read original article]() Published: 2025-10-24T12:00:00Z Content type: release Language: en Sources: [Mistral AI Blog]() Topics: [AI Platform](), [Artificial Intelligence](), [MLOps](), [Security](), [DevOps](), [data](), [Large Language Model](), [Deployment](), [Prompt Engineering](), [on-prem](), [VPC]() Tags: [agents](), [ai](), [ai-adoption](), [ai-assistants](), [ai-platform](), [ai-studio](), [autonomous-agents](), [compliance](), [infrastructure](), [multimodal-ai](), [on-prem](), [privacy](), [provenance](), [vpc](), [workflows]() ### AI overview Mistral introduces AI Studio, an enterprise AI platform for customizing, fine-tuning, evaluating, governing, and deploying AI assistants, autonomous agents, and multimodal AI with open models. It addresses the gap between AI prototypes and production by providing evaluation, feedback and dataset workflows, provenance and versioning, governance, and flexible deployment across hybrid, VPC, and on-prem infrastructure. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Mistral Compute DevFeed: [Mistral Compute]() Original publisher: [Read original article]() Published: 2025-06-11T11:30:00Z Content type: article Language: en Sources: [Mistral AI Blog]() Topics: [AI Infrastructure](), [AI Platform](), [Sovereign AI](), [GPU](), [Orchestration](), [Hardware](), [Tooling](), [Frontier AI]() Tags: [agents](), [ai](), [ai-infrastructure](), [ai-platform](), [autonomous-agents](), [frontier-ai](), [gpu](), [hardware](), [infrastructure](), [models](), [multimodal-ai](), [open](), [orchestration]() ### AI overview Mistral AI announces Mistral Compute, a private and integrated AI infrastructure offering spanning GPUs, orchestration, APIs, products, and services. Customers can deploy it from bare-metal servers to fully managed PaaS and use it to build, train, and serve AI workloads while retaining control of their environments. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## What is agentic AI? An introduction to autonomous agents DevFeed: [What is agentic AI? An introduction to autonomous agents]() Original publisher: [Read original article]() Author: Manish Hatwalne Published: 2025-05-29T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [AI Development](), [Architecture & Design]() Tags: [agentic-ai](), [agentic-ai-applications](), [agentic-ai-architecture](), [agentic-ai-challenges](), [agentic-ai-system-components](), [agentic-ai-vs-traditional-ai](), [agents](), [ai](), [autonomous](), [autonomous-agents](), [beginner](), [components](), [deploying-agentic-ai](), [fundamentals](), [how-agentic-ai-works](), [introduction](), [introduction-to-agentic-ai](), [real-world-uses-of-agentic-ai](), [what-is-agentic-ai]() ### AI overview A beginner-friendly introduction to agentic AI and autonomous agents. It explains how these systems perceive environments, reason, make decisions, and act independently through iterative loops, contrasting them with traditional AI that follows fixed instructions. The article also introduces architectural components, deployment challenges, safety, ethics, and real-world applications. ### Source excerpt A beginner-friendly introduction to agentic AI, its core components, challenges, and real-world uses. Read on. ## How to Write an Agent DevFeed: [How to Write an Agent]() Original publisher: [Read original article]() Author: Simone Margaritelli Published: 2025-03-13T01:36:08Z Content type: tutorial Language: en Sources: [evilsocket]() Topics: [AI Development](), [function calling](), [LLMs]() Tags: [adk](), [agent](), [agent-development-kit](), [agent-evals](), [ai](), [autonomous-agents](), [blog](), [cybersecurity](), [evals](), [evaluations](), [function-calling](), [how-to](), [howto](), [llm](), [nerve](), [nerve-adk](), [project-release](), [tool-use]() ### AI overview This tutorial explains how software agents use models to select tools in a loop, and how function calling lets language models invoke tools. It introduces Nerve as a project intended to simplify implementing an agent and discusses executing tool calls and returning their outputs to the model. ### Source excerpt Hello friends. This blog post was supposed to be the second part of this re ## Multi-agent Workflow patterns and modern use cases | Temporal DevFeed: [Multi-agent Workflow patterns and modern use cases | Temporal]() Original publisher: [Read original article]() Author: Clair Byrd Published: 2024-12-03T08:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Orchestration](), [AI Development](), [real-time]() Tags: [agent](), [autonomous-agents](), [how-to](), [multi-agent](), [orchestration](), [real-time](), [state-management](), [workflow]() ### AI overview This article explains multi-agent workflows as coordinated processes involving multiple autonomous agents. It describes their characteristics, including decentralization, communication, autonomy, interdependency, and state management, and explains how Temporal supports them through orchestration and workflow state management. ### Source excerpt Learn how Temporal powers multi-agent workflows with orchestration, state management, and real-time communication, without custom code.