Topic
All digests tagged AI Agents
AI agents need a 'context engine' to move beyond simple information retrieval. Learn how to fix the context gap, avoid the 'curated context trap,' and achieve massive token and time savings in your CI/CD pipelines.
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Learn how LinkedIn scaled AI agents to thousands of tools and playbooks using a Search/Get Schema/Execute meta-tool layer, enabling reliable, context-aware automation.
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The Multiplayer AI Manifesto outlines five principles for collaborative agent work: agents must live next to the work, learning must be open, and sessions must persist in the cloud. This shifts AI from single-user chats to true co-working.
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AI agents are killing local dev environments and mandatory code reviews. Cloud-based platforms like AMP/Orbs enable massive parallelism, making secure, scalable development possible.
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Learn six essential AI concepts—from LLMs to RAG and Agents—using simple human analogies. Understand how modern AI systems work by connecting the 'brain' to its tools.
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Running AI agents in production requires shifting focus from root cause analysis to context. Key insights cover using combined data sources (Elastic logs + ServiceNow) for diagnosis, optimizing GPU usage during tool calls, and building AI-powered tracing frameworks.
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Coding agents succeeded because their infrastructure was built for automation. Knowledge work lacks these primitives—Centralization, History, Context, Verification, Governance, and Reversibility—creating a massive gap in reliability.
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The shift from CI/CD to Continuous AI is explored, detailing how agents can auto-merge PRs and how fixing the system, not the code, drives efficiency.
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Discover how modern software factories use AI agents, skills, and loops to achieve unprecedented development velocity. Learn why context engineering is the critical step before automation.
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Agentic video understanding with Gemini processes long videos efficiently by using specialized tools (like get transcript and get frames) instead of submitting the entire content, drastically reducing token count.
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Agentic commerce requires a new infrastructure layer. The talk details why traditional financial rails fail at machine speed and emphasizes that robust compliance screening is mandatory for enterprise adoption.
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AWS is enabling agent e-commerce by introducing programmatic payments via protocols like X42. Learn how AgentCore Payments provides secure, guardrailed wallets for AI agents, while WAF allows publishers to monetize bot traffic at the edge.
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AI agents are stalling at paywalls. Circle argues that traditional payment rails fail because agents require high-frequency, microtransactions. The solution: USDC and Nano Payments for sub-second settlement.
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AI News & Strategy Daily | Nate B Jones
AI agents can find zero-day vulnerabilities, but their utility depends on defining 'what done means.' Learn why process isn't value and how to govern agent output.
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In order to get reliable, uninterrupted access to the public web data on the internet, the knowledge layer needs to get the information from specialized tools. This is where the web data infrastructure layer comes into play.
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Translating messy operational knowledge into executable agent workflows requires building a robust 'refining loop' around the AI agents. The system's true asset is the SOP corpus, which captures global process variations and drives systematic improvement.
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Agents are where microservices were in 2015. We discuss how to build reliable, stateful agents by focusing on skills as context units, using hooks for observability, and implementing guardrails at every tool call.
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AI agents have transitioned from model problems to distributed systems problems. Reliability requires incorporating concepts like idempotency keys, circuit breakers, and scoped credentials.
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Ungoverned skills are a form of technical debt. To scale AI-native organizations, know-how must be managed via a centralized skills catalog that enforces versioning, ownership, and composability.
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Tessl Code Review addresses the bottleneck of trusting AI-written code by reading PR intent, using customizable 'review lenses' scoped via globs, and allowing teams to own their review standard in a versionable repo file.
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