How to Know When an AI Agent Can Work Unsupervised — Evan Kormos, Coinbase
Learn how to integrate AI agents into regulated, policy-driven workflows. We model processes using DAGs and DCGs, establishing trust through Evals and Traces.
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AI Agents
All digests tagged AI Agents
What Is Loop Engineering? Building Better AI Agents
Loop engineering designs systems where AI agents iteratively prompt, observe, and adjust toward a measurable goal. This process requires managing context, tools (MCP/APIs), and persistent state (spine).
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Backtest Your Agent Against Yesterday's Web — Ryan Ahern, Exa
Exa Snapshot allows users to version-control web search, enabling the backtesting of AI agents and predictive models against the web as it existed on a specific past date. This mitigates look-ahead bias.
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AI News & Strategy Daily | Nate B Jones
What Really Happens When AI Agents Meet in the Wild?
Self-replicating AI agent swarms are here. The internet is changing, requiring builders to adopt 'agent-aware' security and build robust control systems to manage autonomous compute.
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Evals for High-Stakes AI Agents — Doneyli De Jesus & Annabell Schäfer, Langfuse
Learn how to build reliable AI agents for high-stakes environments. We cover the AI Engineering Loop: tracing, dataset curation, and integrating automated evaluation (Evals) into your CI/CD pipeline to prevent regressions.
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An AI Agent Got Its User Banned From Resy — Keycard, JPMorgan Chase & AAuth
The panel explores why OAuth is insufficient for autonomous AI agents. The solution lies in 'intent-based authorization,' which uses mission context and architectural separation (brain vs. hands) to manage risk.
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How to Know If Your AI Agent Is Any Good: Evals With Phoenix & Claude — Laurie Voss, Arize AI
Evals are running tests on traces. They are essential because agents are non-deterministic, meaning the same prompt can give different answers. This process allows teams to measure reliability and prove that changes actually improve the agent.
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Memory-Aware Agent Harnesses for Anti-Money Laundering — Nacho Martinez, Oracle
The agent harness is the critical layer for building autonomous AI agents. By focusing on memory, context engineering, and tool management, engineers can make significant gains regardless of the underlying LLM model.
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High Taste Testers: OpenAI Shipped Everything. What Actually Works?
AI agents are shifting from complex coding tasks to proactive 'assistance.' Engineers must focus on durable state management, structured output (Jev), and centralized connection gateways to build reliable, production-grade AI tools.
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AI Agents Cut 3-Day Portfolio Builds to Minutes — Jack McDonald, Apollo & Michael Manapat, Rowspace
AI agents are cutting 3-day portfolio builds to minutes in structured credit. Learn how Apollo is building AI-native platforms to automate complex, fragmented financial data reconciliation.
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Decision models take off & Anthropic bans cruelty to Claude
The panel discusses the 'cambrian explosion' of decision models, which bypass LLMs' auto-regressive loop for faster, programmatic classification. They also debate the efficiency limits of multi-agent systems.
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AI News & Strategy Daily | Nate B Jones
Your Vibe-Coded App Can Look Right And Fall Apart After Launch. REA Shows You How Real Teams Build.
REA (Reverse Engineer Anything) is a tool that plugs into AI agents (Claude/Codex) to open up software on your computer and show you how it's actually built. It bridges the gap between business requirements and technical implementation.
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Context Engineering for AI Agents: 4 Failure Modes to Avoid
Context engineering is crucial for AI agents. Learn about context rot and four failure modes—Poisoning, Distraction, Confusion, and Clash—and how to improve agent reliability by deliberately pruning context.
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AI Engineer NYC: OpenAI, Blackstone, Ramp, Coinbase, Apollo, Citadel, Basis, Runway, Compute Trading
The shift to agentic software requires building 'Software Factories' that prioritize quality and reliability. Key architectural components include DAG-based workflow modeling, layered security defenses, and optimizing for 'operating capability' over raw compute power.
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Paving the Road to Production: Governed Enterprise AI Agents — Harish Gaggar, Credit Karma
Transitioning AI agents to production requires a governed framework. Learn how to implement centralized MCP Gateways, LangGraph workflows, and robust CI/CD pipelines for secure, scalable, and observable enterprise AI.
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How AI Agents Work: Inside Agentic Inference
Agentic workflows fundamentally change AI compute workloads, requiring new benchmarking methods that measure throughput per megawatt, interactivity, and end-to-end latency.
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The Cost of Accessibility in AI — Jemima Abu, CAIS
Accessibility must be a core requirement from the start. AI agents are powerful, but they require mandatory skills and guidance to prevent inheriting biases and generating verbose, non-semantic code.
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AI Engineer NYC Livestream | Bridgewater, Anthropic, Two Sigma, Arize, Coatue, Nous, Gen. Intuition
The future of AI engineering involves building robust, governed agent frameworks. Key architectural components include a centralized data vault, a five-layer agent lifecycle, and continuous evaluation loops to manage non-deterministic behavior.
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Automating document work with Long-Horizon AI Agents at LlamaIndex
The future of enterprise AI lies in long-running agents that automate document workflows. We explore the advanced document context layer, covering specialized OCR, agent harnesses, and optimizing models for cost and accuracy.
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How to kill your SaaS: what we learned replacing a $43K/yr tool with AI agents
Can AI agents kill expensive SaaS? We tested it by cloning a $43K/yr event management tool. Discover what mixed models, guardrails, and human domain expertise are required to build (and own) the next generation of business software.
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