Ai-Agents
39 practitioners working with Ai-Agents:
10 AI Agent Failure Modes: Why Agents Break in Production
The documented ways AI agents fail: hallucination cascades, context overflow, tool calling errors, and 7 more. Diagnosis patterns and fixes for each.
ACE Framework for Personal AI
Implement David Shapiro's six-layer cognitive architecture to give your Claude Code assistant mission, values, and strategic context
Adam Azzam's ControlFlow Framework
How Prefect's VP of Product built ControlFlow and Marvin, bringing workflow orchestration and failure handling to AI agents with native observability
Agent Checkpointing: Save, Restore, and Rewind Autonomous Work
How checkpoint systems enable long-running agent workflows by saving state periodically, allowing recovery from failures and rollback from bad decisions
Agent Guardrails: Input/Output Validation for Autonomous Systems
How to implement runtime guardrails that validate agent inputs, filter outputs, and enforce business rules. Covers NeMo Guardrails, layered checking, and production patterns.
Agent Memory Systems
How AI agents implement memory: short-term context, long-term storage, vector retrieval, and the architecture that ties it together.
Agent Observability
How to implement distributed tracing, logging, and monitoring for AI agents using OpenTelemetry and purpose-built tools like Langfuse and Braintrust.
Agentic Design Patterns: ReAct, Reflection, Planning, Tool Use
When to use ReAct loops, self-critique, task decomposition, and tool calling in AI agents. A practical pattern library for building effective agent systems.
AI Memory Compression
Techniques for compressing AI observations into retrievable semantic summaries that fit in context windows
Browser Agents
AI that clicks, types, and scrolls your browser autonomously to complete web-based tasks
Build Your First Browser Agent with browser-use
Set up AI-powered browser automation in Python. Install browser-use, configure your LLM, and run your first web task in under 10 minutes.
Building Your First MCP Server
Create custom MCP servers to extend Claude with your own tools
Checkpointing - Safe Rollback for Agent Work
How automatic checkpoints protect your work and let you recover from agent mistakes
Context Rot: When More Tokens Mean Worse Results
LLM performance degrades predictably as context windows fill up. Learn why this happens, how to detect it, and practical strategies to maintain output quality.
Context Window Management
Keep your AI sharp by managing what fits in its working memory
Episodic Memory for LLM Agents
Give AI agents memory of specific past events with temporal context. The missing piece between semantic facts and procedural rules in the CoALA framework.
Git Worktrees for Parallel Agents
Run multiple AI agents on the same codebase without conflicts using git worktrees
Human-on-the-Loop
Move from approving every AI action to supervising agents that act autonomously, escalating only when confidence drops or risk rises.
Keep Claude Running for Hours
Build autonomous development loops using todo files, hierarchical subagents, and context compaction to process task lists for hours without human input.
LLM-as-Judge Evaluation
Use LLMs to evaluate LLM outputs. Build reliable automated judges through critique shadowing and iterative calibration with domain experts.
MCP Server Composition
Connect your AI agent to multiple MCP servers at once, combining calendar, database, files, and search through one protocol
Memory Attribution and Provenance
Track where AI memories came from, when they were created, and how much to trust them
Memory Consolidation and Forgetting
How AI agents consolidate short-term observations into long-term storage using sleep-inspired patterns, plus when and what to forget.
Multi-Agent Coordination: How to Run Three AI Agents Without Merge Conflicts
The four-phase workflow for running parallel AI agents on the same codebase using tmux, git worktrees, and a shared AGENTS.md file.
Multi-Agent Knowledge Management
When a single AI can't handle your PKM needs, specialized agents working together can automate capture, processing, and synthesis.
Preference Learning: AI That Adapts to You
How AI systems infer your preferences from interactions and adapt without configuration. Covers POPI, Mem0, LaMP benchmarks, and building preference-aware systems.
Principles for AI Delegation
What to delegate to AI and what to keep human
Prompt Engineering for Agent Coding
Structure prompts that make AI coding agents 30-40% more effective
Sandboxing & Security for AI Agents
How to isolate AI agents using OS-level sandboxing to prevent unauthorized access and reduce permission fatigue.
Self-Evolving Agents
Build AI agents that improve through structured feedback capture, automated evaluation, and continuous retraining loops
Self-Updating Instructions (Procedural Memory)
Build AI agents that modify their own operating instructions based on experience, feedback, and observed failures
Subagent Patterns: Parallel, Sequential, Background
Three dispatch patterns for delegating work to AI subagents and when to use each one.
Task Decomposition for AI Documentation
Break documentation projects into discrete steps to get accurate, consistent output from AI tools instead of hallucinated garbage.
The Architecture of a Personal OS
Personal OS architecture: interface, agent, memory, integration, and tool layers. Build your AI system incrementally in 4 weeks
The Three-Layer Workflow
Match your AI tool to the task: tab completion for most work, agents for multi-file changes, reasoning for architecture
Tool Routing: How AI Agents Pick Which Function to Call
Modern agents route between dozens of tools using semantic matching, LLM-as-router, hierarchical patterns, and fallback chains. Patterns for scoring, selection, and MCP sampling.
Tool Use Patterns: How LLMs Call External Tools
Function calling, MCP protocol, and ReAct patterns for AI tool use. Learn when to use each approach and how to implement them.
Vision-Based Web Automation: Why Screenshots Are Replacing Selectors
How computer vision and multimodal LLMs enable browser agents that see pages like humans instead of parsing brittle DOM structures.
What is a Personal Operating System?
What is a personal operating system? Learn how AI agents like Claude Code manage your tasks, memory, calendar, and decisions autonomously