Agentic Engineering
Coding agents, orchestration, evaluations, tool use, guardrails, and autonomous software-production workflows.
AI is moving from suggesting the next line of code to planning and executing entire bodies of work. This topic follows that transition through coding agents, orchestration patterns, evaluation systems, tool protocols, context management, permissions, and the human controls that keep autonomous workflows useful.
The emphasis is practical: how these systems behave on real repositories, where their economics break, what security boundaries they require, and which implementation choices produce dependable software rather than impressive demos. Product announcements belong here only when they change how engineers build, supervise, or verify agentic systems.
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Muse Code Needs a Six-Agent Trial Budget
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HiddenLayer Puts Policy Inside the Agent Harness
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Agent Browser Fixes the Queue Before the Model
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llama.cpp Cuts DeepSeek V4 Prefill by 4.92 Seconds
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Octobench Finds a 5-Task Harness Swing
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Supabase Evals Turns Agent Support Into Tests
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LinkedIn Makes Readers Label the AI Feed
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GitHub Stacks the PRs Agents Made Too Large
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Stateless MCP Makes Agent Infrastructure Boring
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Snowflake Wants the Agent Control Plane