Model Context Protocol

$1 AI Guardrails: The Unreasonable Effectiveness of Finetuned ModernBERTs – Diego Carpentero

Apr 16, 2026 · 43:53

Diego Carpentero argues that LLM-based attacks—Prompt Injection, Indirect Injection, Model Internals (gibberish suffix), RAG Poisoning, MCP Exploits, and Agentic Escalation—are now the baseline, not the exception, and that model alignment and human review alone are insufficient. He identifies the core problem as a Zero Trust Gap: LLMs natively lack separation between system controls and data, allowing adversaries to override decisions via malicious instructions in inputs or external content. To build a protective layer, Carpentero fine-tunes ModernBERT—a state-of-the-art encoder with Alternating Attention, Unpadding & Sequence Packing, RoPE, and FlashAttention—into a safety discriminator that classifies prompts as safe or unsafe in ~35 milliseconds with 85% accuracy, all for under a dollar. He walks through the fine-tuning pipeline using the IngetGuard dataset and demonstrates live detection of real attack examples from each vector.

Piloting agents in GitHub Copilot - Christopher Harrison, Microsoft

Jul 26, 2025 · 59:07

Christopher Harrison (Microsoft) demonstrates GitHub Copilot's agent capabilities for AI Engineers, arguing context is key to effective pair programming. He explains Copilot's modes—completion, chat, edit, local agent, and coding agent—and how Model Context Protocol (MCP) enables access to external tools like databases. Harrison shows how Copilot-instructions.md files provide project-specific guidance, and how coding agent runs within ephemeral GitHub Actions environments with strict security (no external access, only write to its own branch). He emphasizes that AI doesn't change DevOps fundamentals: tests still run locally and via PR workflows, and code review remains essential. The lab covers assigning issues to Copilot, configuring MCP servers, and using .instructions files for reusable patterns, with a repeat session at 3:30 PM.

Collaborating with Agents in your Software Dev Workflow - Jon Peck & Christopher Harrison, Microsoft

Jun 27, 2025 · 1:04:06

GitHub's Christopher Harrison and Jon Peck explain how to collaborate with Copilot Coding Agent as a peer programmer, emphasizing that context—through clear code, comments, instructions files, and MCP servers—is key to success. They walk through a lab where Copilot works on assigned issues in ephemeral GitHub Actions environments without internet or write access to the main repo, creating draft PRs that require human review. Harrison advises against being passive-aggressive, advocating specific prompts and instructions files to guide the agent. The episode covers MCP's role in providing external tools, the need for code review and security checks even with AI, and pricing: $39.99/month for Enterprise tier with coding agent. They also highlight .instructions files for reusable patterns and note that coding agent currently supports only tools, not resources.

Exposing Agents as MCP servers with mcp-agent: Sarmad Qadri

Jun 11, 2025 · 18:05

Sarmad Qadri, CEO of Lastmile AI, argues that agents should be exposed as MCP servers, enabling multi-agent composition and async workflows. He introduces mcp-agent, a framework implementing Anthropic's effective agent patterns—augmented LLM, evaluator-optimizer, orchestrator—on top of the Model Context Protocol. Qadri demonstrates an agent that reads a student story, fetches an APA style guide via a fetch MCP server, and writes a graded report, all orchestrated as a durable Temporal workflow. He explains that MCP standardizes tool connections for LLMs, shifting agentic behavior from client-side to server-side, allowing agents to be invoked from any MCP client like Claude or Cursor. The talk covers how this paradigm enables scalable, platform-agnostic agents that can be paused, retried, and scheduled.

Anthropic in the Enterprise — Alexander Bricken & Joe Bayley

Apr 13, 2025 · 20:55

Alexander Bricken and Joe Bayley from Anthropic's Applied AI team argue that enterprise AI implementation often fails due to overengineering, poor data infrastructure, or lack of testing—but industry leaders achieve transformative results with Claude. They detail Anthropic's deployment models (API, cloud partnerships, enterprise solutions) and real-world case studies like Intercom's Fin agent, which solved 86% of support volume using Claude. Best practices include building evals early as intellectual property, identifying intelligence/cost/latency trade-offs based on use-case stakes, and avoiding premature fine-tuning by trying prompt caching, contextual retrieval, and agentic architectures first. They also highlight interpretability research and the Model Context Protocol for reliable AI deployments.