AI Agents — The Video Signal

Jul 6, 2026 · 49:59

Let's build an AI agent - Phil Nash - NDC Copenhagen 2026

This talk demystifies AI agents by building one from scratch, demonstrating how Large Language Models (LLMs), tools, and memory work together in a continuous loop to achieve goals. The core mechanism involves an agent runtime that orchestrates function calls—allowing the LLM to interact with external systems like file systems or calculators. Advanced concepts covered include the Model Context Protocol (MCP) for standardized tool interaction and 'Skills' for progressive disclosure of capabilities, enabling agents to perform complex tasks like self-refactoring.

Key takeaways

Agent Architecture 17:03
An agent fundamentally runs tools in a loop to achieve a goal. This process requires an LLM, external tools (functions), and an orchestration layer (the 'harness') that manages the interaction.
The Agent Loop 28:10
The core agent functionality is implemented in a loop: The model generates function calls $\rightarrow$ The harness executes those functions (awaiting results) $ ightarrow$ The results are fed back to the model for the next step, continuing until the goal is met.
Standardization via MCP 36:00
The Model Context Protocol (MCP) provides a standardized way for agents to interact with services. It separates concerns into Server components (tools, resources, prompts) and Client components.
Progressive Disclosure with Skills 40:05
Skills allow for progressive disclosure of capabilities. Instead of loading all tool declarations at once, the agent only loads a skill's header initially and can request more details (resources, scripts) as needed.

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Jul 1, 2026 · 5:16

CoreWeave ARIA: The autoresearch loop for continuous improvement

CoreWeave ARIA is an AI Research and Iteration Agent designed to automate the full autoresearch loop for continuous model and agent improvement. The demonstration shows how ARIA autonomously forms hypotheses, analyzes prior run results (including raw system metrics and plots), sets filters, and executes new training jobs via Weights & Biases Launch, allowing human users to focus on high-level problem definition.

Key takeaways

Autonomous Research Loop
ARIA autonomously manages the research process by forming hypotheses, running experiments, evaluating results, and executing optimal next actions without constant human intervention. This capability helps models and agents improve continuously.
Run Analysis and Filtering 1:44
ARIA can analyze complex run data—including plots, tables, and raw system metrics—to determine what worked best. It can also set UI filters based on previous sweeps (e.g., setting a filter for 'auto research runs').
Parallel Experimentation 1:18
Users can run multiple ARIA instances in parallel to accelerate the workflow, allowing simultaneous management of different research tracks (e.g., running two distinct ARIA variants).

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Jun 23, 2026 · 42:38

When millions of AI agents meet

This discussion explores the shift from traditional Large Language Models (LLMs) to autonomous AI agents capable of executing multi-step plans, transacting, and delegating tasks. The concept of an 'agentic economy' suggests a future where intelligence is distributed across specialized agents rather than residing in single systems. Key technical challenges discussed include managing agent failure rates, preventing automation bias, securing the system against malicious 'agentic traps,' and developing robust orchestration protocols for complex, real-world workflows.

Key takeaways

Agent vs. Language Model 0:35
An LLM merely provides continuation or a reply to a prompt; an AI agent observes a state of the world and performs actions in an environment (e.g., purchasing tickets, sending emails). Agents use LLMs under the hood but are wrapped in a harness that enables multi-step planning and automation [0:35].
Distributed Intelligence 28:40
The future may not be defined by Artificial General Intelligence (AGI) as a single entity, but rather by 'distributed intelligence'—a 'society of specialists.' This model suggests that specialized, smaller models are cheaper to run and more reliable than one massive generalist model [17:20].
Agentic Security Risks 22:26
The deployment of agents at scale introduces significant cybersecurity risks. These include 'agentic traps' (malicious code or prompts in the environment) and 'dynamic cloaking,' where web pages display different content for humans versus agents, potentially inducing jailbreaking [1:50].

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Jun 22, 2026 · 46:40

Logs Are All You Need: Rethinking Observability with AI Agents

Sherwood Callaway introduces Sazabi, an AI-native observability platform designed to disrupt traditional tools like Datadog by focusing on logs as the primary source of truth. The core philosophy is that in the age of coding agents, engineers should interact with production data via natural language chat rather than complex dashboards. Key technical innovations include using Git for persistent agent memory across multiple threads and implementing a read-only sandbox environment to safely execute investigative tasks.

Key takeaways

Logs are Sufficient: Rethinking Observability 22:20
The traditional 'three pillars' (metrics, logs, traces) are considered overkill for modern agentic workflows. By focusing solely on logs, instrumentation becomes significantly simpler, requiring only basic logging statements, as the platform can reconstruct metrics and traces from log data.
AI Agents Generate Alerts, They Don't Evaluate Them 28:00
Instead of using AI to triage noisy alerts (alert fatigue), Sazabi autonomously generates actionable alerts directly from logs and codebase analysis. The agent determines what is meaningful to the user at runtime.
Agent Memory via Git for Shared State 34:05
Sazabi maintains persistent, shared memory across multiple parallel sub-agents and threads by committing findings (e.g., issue lists, facts) to a dedicated Git branch within the sandbox environment. This allows agents to benefit from collective findings.
Sandbox Isolation and Read-Only Access 30:30
The platform operates in a read-only system with no public internet access, ensuring security. All actions are routed through an isolated sandbox environment (e.g., using `bash` tools) to prevent data exfiltration or unauthorized changes.

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