OpenAI defines agents as systems that independently accomplish tasks on a user's behalf, making autonomy the defining trait of the categoryCITE:E1. In OpenAI's own wording, an agent is "a system that independently accomplishes tasks on your behalf," a definition that puts the emphasis on independent task completion rather than on any particular interface or feature setCITE:E1.
OpenAI draws the dividing line at workflow control, not at conversational abilityCITE:E2. According to OpenAI, applications that integrate a large language model without letting that model control the execution of a workflow — including simple chatbots, single-turn LLM calls, and sentiment classifiers — do not qualify as agentsCITE:E2. The test, in other words, is not whether a system uses an LLM, but whether the LLM is the one steering what happens nextCITE:E2.
Anthropic describes agents architecturally as LLMs that use tools inside a loop, guided by feedback from their environmentCITE:E5. In Anthropic's own phrasing, agents "are typically just LLMs using tools based on environmental feedback in a loop" — a multistep process in which each action's outcome informs the next step, rather than a single one-shot responseCITE:E5.
Function calling, also known as tool calling, is what OpenAI identifies as the mechanism letting its models interface with external systemsCITE:E3. OpenAI states that function calling "provides a powerful and flexible way for OpenAI models to interface with external systems and access data outside their training data," which is the technical basis for an agent reaching beyond a static knowledge base to fetch or act on live informationCITE:E3.
The Model Context Protocol (MCP) is an open-source standard for connecting AI applications to external systems, launched in 2024CITE:E4. MCP's own documentation describes it plainly: it is "an open-source standard for connecting AI applications to external systems," giving agents a standardized interface for reaching tools and data instead of requiring bespoke integrations for each connectionCITE:E4.
OpenAI says its practical guide to building agents was distilled from actual customer deployments, indicating enterprises are already operating agent systems rather than merely planning themCITE:E7. OpenAI describes the guide as one that is "designed for product and engineering teams exploring how to build their first agents, distilling insights from numerous customer deployments into practical and actionable best practices," dated 2025CITE:E7.
Anthropic warns that an agent's autonomous nature brings higher costs and the potential for compounding errorsCITE:E6. Anthropic's own recommendation is direct: "The autonomous nature of agents means higher costs, and the potential for compounding errors. We recommend extensive testing in sandboxed environments, along with the appropriate guardrails," published in December 2024CITE:E6.
Across OpenAI's and Anthropic's own descriptions, the same word — autonomy — appears as both the defining feature of an agent and the source of its central risk. OpenAI's workflow-control testCITE:E2 and Anthropic's tool-use loopCITE:E5 describe how an agent decides and acts on its own, standardized externally via MCPCITE:E4 and technically enabled by function callingCITE:E3. OpenAI's claim that this is already running in customer deploymentsCITE:E7 sits alongside Anthropic's own caution that the same autonomy compounds cost and error risk unless paired with sandboxed testing and guardrailsCITE:E6 — two first-party accounts describing one mechanism from opposite ends.
OpenAI defines agents as systems that independently accomplish tasks on a user's behalf, making autonomy the defining trait of the categoryCITE:E1.
OpenAI draws the dividing line at workflow control, not at conversational abilityCITE:E2.
Anthropic describes agents architecturally as LLMs that use tools inside a loop, guided by feedback from their environmentCITE:E5.
Function calling, also known as tool calling, is what OpenAI identifies as the mechanism letting its models interface with external systemsCITE:E3.
The line OpenAI draws between agents and chatbots — whether the LLM controls workflow execution — is a useful filter for cutting through agent-washing, because it matches what Anthropic separately describes as the actual mechanism: an LLM looping through tool calls on environmental feedback, not a single scripted response. What's worth watching next is whether the autonomy OpenAI says is already running in customer deployments is being paired with the sandboxed testing and guardrails Anthropic explicitly ties to that same autonomy's cost and compounding-error risk. As MCP standardizes how agents reach external tools, the remaining gap between a working agent and a risky one looks less like a technology question and more like a governance one: who is checking the loop, and how often.
Utility-scale solar has become one of the cheapest new power sources, with costs down roughly 90% since 2010, while 2024 deployment volume outpaced every other generation technology. Paired with record-low battery prices, solar-plus-storage is already powering an AI data center in Arizona — though it still falls short of full 24-hour dispatchable baseload.
2026 marks the European Union AI Act's real enforcement start: the AI Office gains fining power over general-purpose AI on August 2, while high-risk system deadlines are pushed to 2027 and 2028. California activates two new laws on January 1 covering frontier-developer safety disclosure and training-data transparency. The federal government moves the opposite direction, ordering a Justice Department task force to challenge state AI laws, naming California's SB 53 as a target.
China's EHang (億航) is the only eVTOL maker actually flying paying passengers today, holding a full Chinese type, production, and operating certificate set and delivering 221 aircraft in 2025. US rivals Joby and Archer remain in certification or pre-launch stages, while Germany's Lilium and Volocopter both went insolvent in 2024–2025, with Volocopter absorbed by a Chinese buyer.