Plain-English explainers for AI agent concepts: tool use, memory, orchestration, evaluation, safety, refusal policy, stopping conditions, and the rest of the agent stack. Written for non-researchers who need to make build vs buy calls.
The word "agent" gets stretched to cover almost anything with a chat box, so the fastest way to understand agents is to look at what they do. Below are thirty AI agent examples, grouped by the job they replace, with…
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Last updated: July 13, 2026. New rounds added monthly through September. The Q2 2026 edition has been folded into this page; see the Q2 recap below for the April to June picture.
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An AI agent runtime is the software layer that actually executes an agent. The language model decides what to do next; the runtime is what makes it happen. It runs the loop of observing the situation, reasoning about…
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There is no single AI agent ROI number you can copy into your business case, and any roundup that hands you one is selling something. The honest version of the evidence is this: where companies concentrate AI on…
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As of mid-2026, there is no single AI agent law to comply with; there is a layered landscape you have to navigate at once. The EU AI Act is in phased enforcement, several US states have their own AI rules, the NIST…
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Prompt injection is the attack where text fed to a model is written to override its real instructions, and you defend against it by assuming no single control will stop it. There is no filter that reliably separates…
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An AI agent implementation goes right when you start with one narrow, well-defined workflow, give the agent least-privilege access to the systems it needs, keep a human approving consequential actions, and measure…
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By 2026, AI agents are no longer a question of whether but of how, and the how looks very different depending on the size of the buyer. A 40,000-person enterprise and a 12-person agency both want agents to do real…
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AI agent platforms diverge on six dimensions that decide fit: how you tell the agent what to do, how you pay, whether the platform is no-code or code-first, how it connects to your tools, whether it supports human…
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The right way to buy an AI agent platform in 2026 is to start from the job you want done, score every vendor on the same short list of criteria, and prove it with a small paid pilot before you sign anything. This…
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Secrets rotation is the practice of replacing an AI agent's credentials, the API keys and tokens it uses to reach models, tools, and data, with fresh ones on a schedule, and retiring the old ones. It sounds like…
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PII redaction for an AI agent is the discipline of finding personal data in everything the agent touches and stripping or masking it before it reaches a place where it should not live: the model prompt, an outbound…
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A compliance audit for an AI agent asks one blunt question over and over: can you prove it? Not "do you have access controls" but "show me who could reach this agent last quarter." Not "do you handle data carefully"…
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The AI agent market grew loud in 2023 and 2024, with new frameworks, labs, and startups announcing roughly every week. By 2026 the noise has started to resolve into structure. Some of the most-hyped names have been…
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Grounding is the practice of tying an AI agent's outputs to verifiable external sources: retrieved documents, live tool results, database records, or structured reference data. Hallucination is the opposite…
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Emergent behavior in AI agents refers to actions, strategies, or outcomes that were not explicitly programmed but arise from the interaction of an agent with its tools, its environment, or other agents at scale. It…
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An AI agent capability maturity model is a conceptual framework that describes, level by level, how much an agent can do without human involvement, how errors are caught, and what conditions must hold for the system…
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Function calling and tool calling describe the same core behavior: a language model emits a structured request for an external capability, and the runtime executes it. The terms are often used interchangeably, but…
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Agent reflection is the process by which an AI agent evaluates its own output, identifies problems, and revises the result before returning it. It is how agents catch their own mistakes without requiring a human in…
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Long-term memory in an AI agent is any mechanism that stores information outside the active context window so it can be retrieved in a future session. Without it, an agent starts fresh on every run: no record of…
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LLM-based AI agents are probabilistic by default: the same input can produce different outputs on different runs. This guide explains where that variability comes from and the practical techniques teams use to…
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Three open protocols are competing to become the connective tissue of agentic AI systems: MCP, A2A, and ACP. Each solves a different interoperability problem, and understanding the distinction determines whether you…
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Your agent worked yesterday. Then someone tightened a prompt, the model provider shipped an update, or a tool changed its output format, and now it quietly fails one in five of the cases it used to handle. Nobody saw…
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You can score AI agent output quality four main ways, and most teams end up using a blend. Rubric-based scoring rates each output against named dimensions on a fixed scale. LLM-as-judge uses a strong model to apply…
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The big shift in AI agent pricing for 2026 is this: the per-seat subscription, the model that built modern SaaS, is losing its grip. When software was a tool a person operated, charging per login made sense. Agents…
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Most teams decide an agent is "ready" by trying it a few times and getting a good feeling. That is how a demo passes and production fails. An evaluation framework replaces the gut call with a repeatable process: you…
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If you want citable numbers on AI agent adoption, the honest starting point is this: the curve is steep, but the figures depend heavily on how each survey defines an "agent." The baseline is now near-universal.…
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You changed the prompt, swapped the model, or added a tool, and now you want to know if the new agent is actually better. The honest answer is that you cannot know from a few hand-checked examples. Agent behavior…
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Becoming a Gravity builder means one clear thing: you build expert agents to Gravity's quality bar, and Gravity then runs those agents for users and pays you to build and maintain them. You are not setting up a…
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An SLA, a service level agreement, is a vendor's written promise about reliability. The headline number on the page is the part everyone reads. The part that actually decides what you are buying is the fine print:…
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