Topic
All digests tagged AI Agents
Connecting agents is a distributed systems problem requiring solutions for ordered transport, persistence, and runtime binding. BAND introduces an interaction layer to solve the single-agent bottleneck.
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Scaling AI agents requires more than just tools. Success demands redesigning the entire workflow, remodeling roles, and building trust incrementally through automated code review and merging.
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Learn how to build a self-improving company by turning agent failures into reusable skills. We discuss using MCP as the distribution layer and creating an organizational flywheel that captures hard-won knowledge.
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Entire is a new developer platform built to solve the 'missing middle' of the SDLC in the agent era. It captures context, decisions, and evaluations from intent to production, reimagining the PR into 'Trails' and integrating programmable 'Runners' and 'Gates.'
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Building agent quality platforms is a complex systems challenge. Learn why simple spreadsheets fail and how to build the continuous improvement loop using advanced tracing and observability.
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Marc Klingen details the self-improving OSS agent stack, showing how AI can automate the loop between online tracing, offline datasets, and evaluations, moving teams from manual to automated agent refinement.
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Codex is demonstrated as an advanced AI agent capable of automating complex, multi-stage development workflows. Learn how to use Goals, Subagents, and the App Server to build 'software factories' and manage entire CI/CD pipelines.
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The Universal Verifier addresses the unreliability of LLM judges for agent testing. By using structured rubrics and ranking evidence, it significantly closes the gap between perceived and actual agent success rates.
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The talk details the shift from manual trace review to automated, continuous improvement loops for AI agents. By introducing 'Signal,' the system detects patterns in evaluation failures and automatically suggests fixes, closing the software development loop.
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Deep dive into evaluating OCR and AI agents on complex documents. Learn how to perform root cause analysis when an agent fails, tracing the error back to upstream data issues like OCR inaccuracies or missing watermarks.
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As AI agents commoditize implementation, the most valuable skill for engineers is Product Engineering: the ability to define the 'right thing' to build. This requires connecting customer needs to technical constraints, focusing on the smallest useful slice of the system.
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Learn to operate a 'fleet of agents' instead of babysitting single sessions. We cover Goals, Loops, Hooks, and Git worktrees to build fully autonomous, verifiable development workflows.
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LangChain outlines the shift to 'owning your intelligence' by building domain-specific agents. The platform provides Managed Deep Agents, Jev decision models, and LangSmith Engine v2 for robust governance and production readiness.
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Learn about meta-tooling: building AI agents that write, load, and use their own tools at runtime. Discover how to implement self-healing, self-modifying agents using the Strands Agents SDK.
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Keyword search is dying. As shopping moves to the intent and delegation eras, product catalogs must be rebuilt for AI agents. Learn how hybrid search and structured data enrichment are key to success.
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AI News & Strategy Daily | Nate B Jones
AI agents are forcing a re-evaluation of software value. The focus shifts from the application interface to the underlying data, the application workflow, and the agentic layer. Learn how to build for the future.
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Learn how AssemblyAI built 'Joey,' an AI agent that boosted ticket resolution from 10% to 80%. Discover the architecture using local markdown, embeddings, and the Claude Agent SDK.
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Dashboards are dead. The future of enterprise tooling belongs to AI agents. Learn how to build systems that perform complex, cross-application workflows by designing for agents, not for human clicks.
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AI News & Strategy Daily | Nate B Jones
OpenAI's DevDay launches Dots, Space, and GPT-6.1 Sol, accelerating the 'agent wars.' The focus shifts from form factor to utility, emphasizing how context and specialized APIs (like Decisions) enable small teams to achieve ambitious, automated outcomes.
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Discover how VS Code moved from monthly to weekly releases by rebuilding its entire system around AI agents. Learn about improving CI/CD with TypeScript Go, automating issue triage from 51 billion daily events, and implementing self-correcting feedback loops.
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