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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OpenAI Decisions API Costs 2.38x Jev's Token Rate
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EmbeddingGemma 2 Shrinks the Code Retrieval Vector
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Mistral Large 4 Gives Buyers a One-Month Exit Clock
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Ghost Core's $3,499 local-agent test
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Cloudflare AI Search Halves the Semantic Free Tier
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Restate's Agent Pitch Has a $5 Break-Even Test
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SkillSeek's 46% Saving Needs a Matched Accuracy Check
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Atlas Infinite's 128 TB Preview Lacks Vector Search
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Cribl's Free AI Pitch Has a 43% Larger Model Roster
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Sonnet 5.5 Cuts Short-Prompt Reuse Cost by 32.5%