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All digests tagged Agentic Engineering
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The next leap in agentic engineering is moving from Human In the Loop to Out the Loop. This requires building durable, proactive agents in isolated sandboxes, mastering the Core Four (Context, Model, Prompt, Tools), and building the system that builds the system.
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Agentic Engineering is the future of software development. Learn the five core concepts—Prompt, Context, Harness, Loop, and Graph Engineering—that power autonomous AI agents and automate the entire SDLC.
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The video details building a Durable News Agent to filter AI noise. By moving from 'in the loop' to 'out the loop' using proactive agents, engineers can build autonomous systems that gather, filter, and curate high-signal information without constant supervision.
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Pi Durable revolutionizes agentic engineering by creating proactive, persistent agents. Learn how these durable agents manage state, automate incident response, and operate autonomously across multiple clients, moving development from reactive to proactive.
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OpenRouter usage shows token spend is up 80x year-over-year. The key to AI success is 'tokconomics': optimizing workflows using specialized models like Jev alongside LLMs to maximize value per token.
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Kimchi is an open-source coding harness that optimizes LLM usage by measuring cost per task, not cost per token. It features Ferment for autonomous, milestone-based coding and Teleport for remote, continuous agent sessions.
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Jev by TypeSafe is a revolutionary tool for agentic engineering. It provides intelligent, JSON-programmable decision-making, acting as a cost-effective 'third primitive' to enhance agent safety, context management, and file analysis at scale.
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In the age of AI, data science skills—like handling noisy, non-deterministic signals—are more valuable than ever. Human judgment is critical for measuring AI output and designing robust RAG systems.
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Model selection is a 3D problem (performance, cost, speed). To accurately benchmark agents, focus on five specialized tests: Terminal-Bench, APEX Agents, AutomationBench, AA-Omniscience, and DeepSWE.
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IndyDevDan demonstrates three multi-agent swarms (GLM 5.3, DeepSeek v4 Pro, Gemini 3.7 Flash) on an M4 Mac mini to prove that agent swarms are viable for complex engineering tasks when properly engineered.
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The Agentic Operating Level is a framework that maps where engineers should focus attention, balancing leverage/speed (high) against control/understanding (low). Higher is not always better.
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Building reliable voice agents requires complex cascaded pipelines (STT $\rightarrow$ LLM $\rightarrow$ TTS). Engineers must manage latency trade-offs, context limits, and model fallbacks to achieve human-like performance.
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To scale your software factory, move it into agent sandboxes. This provides isolation, scale, and autonomy by implementing a three-tier orchestration system.
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The key to advanced agentic engineering is realizing that agents plus code beats agents alone. The Super Simple Software Factory automates the full SDLC using deterministic checks and three principles: Observable, Customizable, and Reusable.
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Software engineering is shifting from deterministic instructions to defining goals for AI agents. Agentic Engineering emphasizes human oversight in managing probabilistic workflows, making the developer an orchestrator rather than a manual coder.
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Anthropic may not be stealing your data, but their use of anonymized aggregate usage data creates a 'market map,' allowing them to compete directly with their customers. Learn how to achieve AI sovereignty.
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The winning mindset in AI is 'AND,' not 'OR.' Model Fusion combines compute from multiple state-of-the-art agents (e.g., GPT 5.6 Sol, Claude Fable 5) using custom commands like /opinion and /auto validate to achieve superior engineering results.
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Forget loop engineering. Agentic Engineering is about building AI developer workflows inside your software factory, orchestrating engineers, agents, and deterministic code.
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Learn how to solve multi-agent orchestration problems using programmatic tools like Cmux, focusing on three-tier agent architecture and achieving true agentic access.
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Engineers must stop picking single models. The key to modern AI architecture is 'model stacking,' using open-weight options like GLM 5.2 and MiniMax M3 to balance performance, speed, and cost.
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