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All digests tagged Agentic Workflow
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Managed Deep Agents streamlines the journey from local agent prototype to production. It couples the agent harness with LangSmith infrastructure, simplifying complex tasks like durable execution, multi-tenancy, and context governance.
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CoreWeave Agent Lens delivers intelligent agent observability at enterprise scale, providing a single view of every trace to turn production issues into proven fixes.
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Exa Snapshot enables version-controlled search, allowing agents to search the web as it existed on any past date. This is critical for backtesting predictive signals and preventing model overfitting.
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Learn how to build a 'software factory' using agent feedback loops. We cover inner, middle, and outer loops, and why clear intent is crucial for self-healing codebases.
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AI is making PRs bigger and more numerous, turning CI/Code Review into a bottleneck. Discover agentic validation: an automated system that reviews, autofixes, and auto-merges PRs, keeping your development velocity high.
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Codex Cloud allows agents to run tasks in managed environments, eliminating the need for local infrastructure upkeep. The system supports private resource access using Tailscale and OIDC, enabling secure, scalable development.
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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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AI is changing DevEx: PR size is up (44 to 72 lines), but Change Confidence is down 6%. The true bottleneck isn't code generation, but meeting overhead and context switching.
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Antigravity 2.0 introduces agent teams, powered by dynamic subagents, sidecars, and generative UI. This architecture allows complex tasks, like building an OS kernel, to be automated by swarms of specialized agents.
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Learn about the next generation of voice AI, covering native audio models, conversational latency, and agentic tasks. We discuss moving beyond WER to measure real-world dialogue quality.
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SmithTune provides a single, end-to-end workflow to convert agent traces into custom fine-tuned models. The process includes dataset creation, training (using providers like Fireworks or Baseten), and evaluation.
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Spec-Driven Development (SDD) is the disciplined alternative to 'vibe coding.' It teaches developers to write detailed specifications (the 'what') to guide coding agents, ensuring maintainable code and structured feature development.
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Learn how to build Patch, an agent that converts Slack messages into working GitHub PRs. This walkthrough details using Managed Deep Agents to integrate Slack, GitHub, and a sandbox with minimal code.
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This devlog details building a self-compacting Pi Agent to solve context window limits in autonomous agents. By giving the agent a dedicated tool and three compaction thresholds (Notice, Warning, Force), engineers can achieve reliable, out-of-loop agentic coding.
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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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Lada Kesseler details 'loop engineering' for AI agents, arguing that quality is achieved through iterative refinement (the centrifuge). She advises using deterministic verifiers and structured ground rules over expecting perfect first drafts.
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The most flexible system wins. To outperform individual LLMs, engineers must combine compute using multi-agent orchestration patterns: Opinion, Debate, and Collaborate.
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OpenWiki is an open-source CLI that generates documentation specifically optimized for coding agents. It uses OKF and GitHub Actions to automatically maintain docs synced with your repo's Git history, improving agent efficiency.
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Learn how LinkedIn built a hiring agent using LangGraph to cut time-to-interview by 60%. We cover the shift from static workflows to advanced plan-execute-replan loops and essential harness engineering techniques.
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Patrick Debois maps AI-native development maturity, outlining a progression of patterns (Prompting $\rightarrow$ Context $\rightarrow$ Harness $\rightarrow$ Loop) and emphasizing that organizational success requires platform enablement and measuring shared component contributions.
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