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All digests tagged AI Agents
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Arize's agent Alex evolved from a 50% hallucination prototype to a robust system. Key lessons include moving planning into custom harness code, treating large tool results as files, and using small markdown files for long-term memory.
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Code Mode is an alternative architecture for AI agents that uses a programming sandbox instead of a large tool catalog. This approach drastically reduces context bloat, lowers token usage, and enables emergent behavior by allowing agents to compose tools in code.
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Fuad Ali details the self-improvement loop for AI agents: trace failures, use Signal to detect issues, create living regression datasets, and run experiments to prove fixes. This automates reliability.
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Harbor is an open-source framework for evaluating AI agents on real tasks using isolated sandboxes. It allows engineers to test agent changes by measuring side effects and optimizing metrics like tool call count, moving from 'vibes' to 'evidence.'
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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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Managed Agents run coding agents in secure cloud sandboxes via a single API call, enabling scalable, multi-agent workflows for tasks like financial research and reporting.
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AI agents have made writing code nearly free, shifting the bottleneck from coding to defining the problem. The future of engineering lies in the Product Engineer role: figuring out what 'good' means.
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To ensure AI agent reliability, the speaker details a five-step regression loop. This process uses production traces as the source of truth, employing LLMs as judges to quantify subjective metrics and integrating automated gates into CI/CD.
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Bridgewater's Vault processes 100M+ documents, transforming unstructured data into a first-class asset. Learn how they built for both human precision and agent scale using advanced filtering and proactive AI agents.
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AI News & Strategy Daily | Nate B Jones
AI agents are accelerating cyberattacks, making stolen API keys and foundational vulnerabilities (PHP, eMudhra) critical risks. Learn how to audit your systems and build stronger defenses.
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Learn how to use AI 'dots' to automate bug tracking and triage. See how an engineer uses an agent to gather feedback from Reddit and Twitter, identify major bugs, and initiate root cause analysis.
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Arca's AI-native approach uses long-horizon agents to run a financial services business. The system manages complex, multi-day workflows by integrating signal systems, persistent memory, and shared spaces.
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The future of AI agents requires 'babysitters.' We discuss moving from simple API calls to auditable, intent-based workflows using Jev and WebMCP, and how to verify business outcomes with tools like Cucumber.
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Experts discuss the risks of self-replicating AI 'digital slime molds' on IoT devices, the shift of medical AI to treatment decisions, and the complex coordination of autonomous AI agents.
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Learn how to build agent-ready apps using open standards. We cover the auth.md protocol, ID-JAG identity assertions, and scoped tokens for secure agent access.
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When public benchmarks are unavailable, the solution is to reverse the evaluation process: start with the known answer and generate the input. This shifts AI agent testing from slow expert review to automated, scalable code checks.
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GEICO tackled the 'outer loop' bottleneck in SDLC by building an AI-native war room. This system uses agent fleets and structured rules (FleetKit) to accelerate major code modernization missions and automate decision-making.
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Monzo built an in-house AI coding agent that authors 10% of PRs. Success came not from the LLM, but from building a robust, composable platform with an MCP gateway and sandboxed orchestration.
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The agentic era is redefining the web. AI agents are moving beyond chat to autonomously book trips, handle bureaucracy, and make payments, forcing a re-evaluation of e-commerce data pipelines and digital security.
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AI News & Strategy Daily | Nate B Jones
Running AI agents requires more than just a bigger chip. Learn how to audit your machine's memory and storage, manage local resources, and optimize your workflow to maximize compute power.
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