Pages tagged "ai-agents"

17 pages tagged with ai-agents. ← all tags

• AI Automation — Overview The domain front door: what AI automation reliably delivers (1,048-case evidence), the automation-eats-execution thesis, agent-reliability patterns, and how to find and stage your own use cases.
• Ad Platforms Ship Agent Control Surfaces + AI Creative (Mid-2026) In May–July 2026 TikTok, Reddit, LinkedIn, and Meta shipped agent-facing ad APIs (MCP), on-platform AI creative generation, and brand-inference layers (Meta Brand Memory). TikTok's Agentic Hub has full write access; Google's Ads MCP stays read-only (re-verified July 12). AD-Bench gives the first real-world agent-reliability figures for ad tasks: best model 76.9% Pass@1.
• Agent Payment Protocols — The Infrastructure Under Agentic Commerce Four standards shipped in 2025-2026 define how AI agents transact: AP2 (Google's universal payments rail, September 2025), x402 (Coinbase/Cloudflare HTTP-native stablecoin protocol), UCP (Universal Commerce Protocol — the commerce schema), and Visa TAP (agent identity/verification). AWS Bedrock AgentCore Payments shipped May 7, 2026, making x402 hyperscaler-native. The infrastructure layer for agentic commerce is now production. The protocols are layered, not competing in most cases.
• Agentic Commerce — The $1 Trillion Shift How AI agents are transforming shopping: market projections, business model changes, infrastructure protocols (AP2 / x402 / UCP / Visa TAP shipped Sep 2025 – May 2026), and what merchants must do to prepare. The infrastructure layer is now production.
• AI Agent Behavior — What It Means The emerging field studying how AI agents make decisions, including purchasing biases. Connects to the jagged-frontier and recognition-primed-decision foundations: agent biases are predictable from the same conditions that predict human pattern-matching reliability.
• Agentic Memory — How AI Agents Remember Across Sessions Agentic memory is the architecture that lets AI agents retain useful information across sessions, tasks, and time. The category covers everything from simple conversation history to learned skills, persistent project context, and cross-session preference accumulation. Memory is the difference between an agent that re-explains itself every time and one that compounds context over weeks. The discipline of designing memory well is part of agent engineering. Anthropic's Dreaming (May 2026) is the first platform-level realization of automated semantic+procedural memory consolidation.
• Agentic Search — How AI Agents Decide Which Brands Get Found Understanding agentic search and how AI agents evaluate, compare, and recommend brands on behalf of users
• Context Engineering — Designing Information Flow for AI Agents How to structure tool responses so AI agents can reason effectively across multiple calls
• Agentic Search Optimization (ASO) — The New SEO How to optimize your brand for AI agents that search, compare, and purchase on behalf of users. May 2026 update with E-E-A-T binary filter finding (96% of AI Overview citations come from strong-E-E-A-T sources), brand-mentions-3x-stronger-than-backlinks correlation, and Google AI Mode adoption (75M daily users).
• AI Humor and Forgiveness — Self-Deprecating Humor as a Service-Failure Recovery Tactic When an AI agent makes a mistake, humorous responses make users more forgiving — and self-deprecating humor outperforms positive humor by a wide margin. Xie et al. 2025 (n=1,919, Journal of Business Research) found +47.8% forgiveness uplift for self-deprecating vs no-humor on low-severity errors. The effect disappears for high-severity failures and inverts when the customer is the focal victim (Honora et al. 2025, J. Business Ethics) — humor reads as sarcasm and reduces perceived company morality. The 2026 practitioner gate: low severity + non-focal-customer-burned + AFTER resolution = humor helps. Otherwise: stay sincere.
• Guardrails — The Production Safety Layer for AI Systems Guardrails are the technical and operational controls that bound what AI agents can do, when, and with what permissions. They are the production safety layer that sits between a capable model and the real world. Without guardrails, capable agents become uncontrollable. With well-designed guardrails, the same agents are reliable enough to ship. Guardrails are paired with tool use — every powerful tool needs a corresponding guardrail.
• Tool Use — How AI Agents Reach Out of the Model Tool use is the capability that lets an AI model call external functions — search, calculators, APIs, databases, code execution — rather than generating answers from training alone. Tool use is what turns a chatbot into an agent. It is the technical foundation under all agentic-commerce, agent-engineering, and managed-agents work. The discipline of designing tools well determines whether an agent is reliable or theatrical.
• Agent Adoption Frictions — Three Psychological Barriers Wharton's 2026 framework: AI agent adoption is blocked by three psychological frictions — perceived competence, trust, and delegation of control. The barrier is not the technology; it's whether users will hand over the keys.
• Agent Engineering — Karpathy's Ceiling-Raising Discipline Andrej Karpathy's framing (Sequoia AI Ascent 2026) for the professional discipline of coordinating AI agents reliably and safely — distinct from vibe-coding. Vibe-coding raises the floor of who can build software; agent engineering raises the ceiling of what professionals can deliver.
• Agentic AI vs Generative AI — Which to Choose Comparison of agentic AI (autonomous task execution) and generative AI (content creation) for business applications
• Cognitive Automation — What It Means Plain-English explanation of cognitive automation for business professionals
• AI Agent Organization — 12 Techniques That Work Practical techniques for turning AI agents into reliable business tools