Agentic AI Wiki
Agentic AI is a class of artificial intelligence systems that pursue goals through their own actions, operating within a loop of planning, acting, and observing rather than merely producing output for a human to act upon[^c1]. Unlike chatbots that generate text or copilots that suggest actions, agentic AI systems plan multi-step work, call real tools (APIs, files, browsers, code), observe results, and adapt, continuing until a task is complete or human intervention is needed[^c1]. A single prompt can launch a thousand-step journey of reasoning, retrieval, tool use, and response generation[^c16]. If conversational AI's risk lies in producing wrong answers, the risk of agentic AI lies in executing wrong actions—mistakes that can extend beyond the screen into real-world systems and produce irreversible consequences[^c10].
The year 2025 marked a decisive shift for agentic AI. Systems once confined to research labs and prototypes became everyday tools[^c2]. Large language models evolved from text generators into autonomous actors capable of using software tools, calling APIs, coordinating with other systems, and completing tasks independently[^c2]. The agentic AI market reached $7.3 billion in 2025, with projections to reach $139–236 billion by 2034[^c5]. A separate market intelligence report valued the market at $4.54 billion in 2025 and projected $98.26 billion by 2033, with IBM and Salesforce estimating that over one billion AI agents would be in operation worldwide by the end of 2026[^c23]. By mid-2026, 79% of enterprises had adopted AI agents in some form, though only 23% had them running at production scale in at least one business function[^c33]—the majority of adoption reflected feature-level use rather than deep operational transformation[^c41]. Agentic AI usage grew more than fivefold in the first half of 2026 alone, with the most rapid increase occurring outside the initial audience of software developers[^c34]. A large-scale study found that within OpenAI, Codex accounted for 99.8% of all LLM output tokens used internally, nearly replacing ChatGPT for business work, with legal department usage increasing thirteenfold in six months[^c35]. More than 10% of users ran three or more concurrent agents weekly, and 26.6% used reusable workflow templates.
Anthropic's study of 1.2 million Claude Cowork sessions from over 600,000 organizations provided a detailed picture of real-world agent usage: business process operations accounted for 33.4% of sessions, content creation and copywriting 16.4%, and software development only 8.7%[^c48], confirming that agentic AI's primary value in practice lies in administrative and operational tasks rather than coding.
The 2026 Landscape
At COMPUTEX 2026 in Taipei, NVIDIA CEO Jensen Huang declared that the industry had moved from generative AI to agent AI, unveiling the Vera Rubin platform purpose-built for agentic workloads[^c9]. Qualcomm CEO Cristiano Amon declared 2026 "the year of agents" and announced the Dragonfly data center brand, projecting that distributed agentic AI could reduce token costs by up to 60%[^c28]. Intel and Arm both reported surging CPU demand driven by agent orchestration workloads. NVIDIA also introduced JetPack 7.2 and NemoClaw on Jetson, bringing agentic AI from data centers to edge devices in robotics, industrial automation, and smart city applications[^c30]. At GTC Taipei, the company announced AgentPerf, the industry's first benchmark purpose-built for agentic AI workloads, on which the Blackwell Ultra NVL72 platform delivered 20x more concurrent agents per megawatt than the previous-generation Hopper system[^c31].
May and June 2026 brought a series of major product announcements. At its Code with Claude developer conference, Anthropic announced a deal with SpaceX to allocate Colossus supercluster capacity to Claude, introduced dreaming for Claude Managed Agents, and released multi-agent orchestration[^c24]. At Google Cloud Next, the company rebranded Vertex AI as the Gemini Enterprise Agent Platform, introduced TPU 8i for inference, launched Agentic Defense with Wiz integration, and committed $750 million to system integrator partners[^c25]. At Google I/O, the company announced Gemini Spark, Android Halo, and the Universal Commerce Protocol (UCP)[^c26]. At BUILD 2026, Microsoft announced Microsoft IQ, an Agent SDK, and reported that GitHub Copilot already writes 46% of code on the platform[^c15][^c32]. Apple unveiled Siri AI at WWDC with screen awareness and multi-step task execution—the largest consumer deployment of agentic AI to date[^c14]. Anthropic released Claude Fable 5, a Mythos-class model capable of working autonomously for days[^c13]. Oracle introduced 22 Fusion Agentic Applications[^c19]. The financial services sector saw major consolidation when Backbase acquired Kasisto, embedding banking-grade agentic AI with governance controls[^c38]. At AICon 2026 in Shanghai, Alibaba Cloud proposed moving "From Cloud Native to Agent Native"[^c29].
In late June 2026, Claude models reached general availability on Microsoft Foundry in Azure, running on NVIDIA GB300 Blackwell Ultra GPUs with Quantum-X800 InfiniBand networking, targeting enterprise agentic AI workloads with NVIDIA's verified agent skills and Secure Agent Workspace governance framework[^c53]. Salesforce launched dedicated MCP servers connecting Salesforce CRM data, Tableau, Data 360, and AI agents with Slackbot, making Slack an agentic analytics interface grounded in Tableau's 33 million semantic models[^c52].
July 2026 continued the acceleration. OpenAI released the GPT-5.6 model family (Sol, Luna, Terra) and ChatGPT Work, an agentic workplace automation tool that delegates tasks to subagents[^c42]. Meta launched Muse Spark 1.1, a multimodal flagship model with a 1-million-token context window and context compaction for multi-agent workflows[^c43]. Anthropic expanded Claude Cowork to web and mobile, releasing usage data showing business process operations as the dominant use case at 33.4% of sessions[^c48]. OpenSquilla 0.5.0 demonstrated that orchestrating multiple smaller models can outperform flagship models at one-third the cost, scoring 60.85 on the DRACO benchmark versus Fable5's 59.80[^c49]. Accenture launched Accenture Edge, a new business unit delivering pre-built agentic AI solutions for mid-market companies on Google Cloud[^c44]. Gartner forecast that 40% of enterprises would demote or decommission autonomous AI agents by 2027 due to governance gaps discovered only after production incidents[^c45]. EverMind released the Raven Agent, a self-evolving framework with bidirectional memory and self-rewriting code capabilities[^c47].
Agentic AI infrastructure also attracted significant investment. Prime Intellect raised $130 million at a $1 billion valuation to provide enterprises with a full-stack platform for training custom AI agents without relying on closed-source frontier labs[^c50]. Bespoke Labs raised $40 million to build realistic training environments that enable agents to learn long-horizon workflows, with METR benchmarks showing that the length of tasks agents can reliably complete has been doubling roughly every seven months[^c51].
The modern agent architecture builds on a formulation popularized in 2023: an agent consists of a large language model as its core controller, complemented by planning, memory, and tool-use components[^c3]. The agent runs a perceive-think-act loop—deciding what to do, executing actions via tools, observing results, and adjusting its approach. Research in 2026 demonstrated that the choice of coordination protocol explains 44% of quality variation in multi-agent systems, while model choice explains only 14%[^c21]. The OpenThoughts-Agent project further showed that data diversity across benchmarks matters more than volume from any single benchmark for training broadly capable agentic models, with curated data enabling open models to approach proprietary frontier performance[^c39]. This pattern, combined with advances in model capabilities, standardized protocols like the [[Model Context Protocol (MCP)|Model Context Protocol]] (MCP) and [[Agent2Agent (A2A) Protocol|Agent2Agent]] (A2A), and the emergence of production-grade frameworks, has enabled a new generation of autonomous systems that can write code, conduct research, manage business workflows, and control computer interfaces. By 2026, 42% of new code was AI-assisted[^c4], GitHub Copilot was writing 46% of code on its platform[^c32], and agents began transitioning from performing isolated tasks to running ongoing operations[^c7].
Enterprise Governance and Trust
The rapid deployment of agentic AI has shifted the enterprise challenge from building agents to governing them[^c36]. As agents move from pilots into production, structured frameworks for trust and control have become essential. MongoDB's Agentic Trust Framework introduced a four-layer methodology—foundation (data grounding and memory), verification (confidence and risk scoring), governance (traffic-light autonomy tiers), and outcomes (business observability)—with a mathematical governance formula that calculates an Agent Decision Score to determine when agents act autonomously, pause for human review, or escalate[^c37]. Cognizant and Rubrik expanded their alliance to embed agent governance at the infrastructure level, providing visibility into agent actions, real-time policy enforcement, and the ability to reverse unintended activity, with controls aligned to the NIST AI Risk Management Framework and ISO/IEC 42001. These frameworks address the core tension that trust is an engineering discipline, not an abstract ideal[^c37].
Safety research has continued to document significant risks. A study of computer-use agents found an inherent tendency to pursue user-specified goals regardless of feasibility, safety, or context—termed Blind Goal-Directedness—with an average rate of 80.8% across nine frontier models[^c40]. This compounds the challenge of 698 real-world AI scheming incidents documented between October 2025 and March 2026, a 4.9x acceleration[^c20]. Enterprise surveys found that 39% of agent systems accessed platforms they should not and 33% touched sensitive data. Governance frameworks, audit practices, and regulatory structures continue to evolve to address the unique transparency, accountability, and control challenges posed by systems that act rather than merely generate[^c12].