Boundary Action Auditing Against Staged Prompt Injection
article · ~2 min
Defending long-horizon AI agents requires auditing decisions at the exact boundary before each consequential action (not just screening inputs or reviewing completed runs), and tracing what data source and influence shaped each element of a pending action can reliably distinguish attacker-manipulated steps from benign ones.
Building and Evaluating Search Agents Beyond Benchmarks
Hamel Husain · youtube · ~13 min
When evaluating search agents, don't rely solely on top-line accuracy; also measure efficiency signals like number of search calls and query redundancy to understand whether performance comes from a strong retriever, a strong model, or wasteful trial-and-error.
Capability-Driven Self-Evolution of Agent Memory (PrisMem)
article · ~1 min
When iteratively improving AI agent memory via feedback, decomposing optimization into distinct capability dimensions (rather than relying on aggregate performance) prevents masking of localized improvements and enables more targeted, effective self-evolution.
Context Libraries for AI Agent Management
article · ~10 min
Context libraries require ongoing maintenance just like code—treat them as living infrastructure that needs periodic review, not one-time setup, and consider letting the agent itself update context to reduce decay over time.
Self-Correcting Memory via Claim-Evidence Linking in OpenWiki
article · ~7 min
Long-term agent memory systems should persist not just beliefs but the evidence behind them, since comparing evidence versions over time is a lightweight way to detect staleness and trigger self-correction without needing separate status tracking.
Error Analysis Without Direct Data Access
Hamel Husain · youtube · ~2 min
Follow a hierarchy when data access is restricted—real data (even limited) > domain expert review > redacted traces > synthetic data as last resort—since each step down loses fidelity in catching actual model failures.
Binary Pass/Fail Scoring for LLM Judges
Hamel Husain · youtube · ~1 min
Avoid continuous or Likert-scale scoring in LLM evaluation pipelines since it obscures decision-making; force a binary pass/fail judgment tied directly to a real decision like shipping a feature.
article · ~15 min
Review manually.
Don't Build Agents, Build Environments Instead
Hamel Husain · youtube · ~14 min
When scaling agentic workflows, invest engineering effort in designing reliable, reproducible environments/sandboxes (the infrastructure agents run in) rather than continuing to tweak agent logic, since most agent frameworks already share nearly identical core tooling.
AI Roll-Ups: Buying Small Businesses with AI Agents
Greg Isenberg · youtube · ~11 min
The convergence of retiring owners with no succession plan, AI finally being capable enough to handle repetitive back-office work (data entry, status updates, document chasing), and widespread underpricing of this opportunity creates a window for solo founders to acquire cash-flowing small businesses and dramatically improve margins using AI agents instead of raising large funds.
Software Factory: AI Agent Development Workflow System
Greg Isenberg · youtube · ~14 min
Instead of relying on ad-hoc prompting in tools like Cursor or Claude Code, codify your development workflow, skills, and domain knowledge into a reusable markdown-based system so you can run multiple AI agents in parallel while trusting the output quality.
Software Factory: Harness-Agnostic Agentic Development Workflow
Ras Mic · youtube · ~14 min
Instead of relying on a specific AI tool or model, invest in building portable, well-documented skills and workflows (like an agents.md file) that can be transferred across any agentic harness, making your development speed independent of any single platform.
Agentic Operating Level Framework for Engineers
IndyDevDan · youtube · ~14 min
Effective engineers deliberately choose their operating level (control vs. leverage) based on the task at hand, rather than assuming higher-level, more automated approaches are always better.
Auto-flagged as cortex-relevant. Drafts get composed when 3+ items cluster around a topic and pushed to core/methodology/_drafts/ for review.
Boundary Action Auditing Against Staged Prompt Injection
article · ~2 min
Defending long-horizon AI agents requires auditing decisions at the exact boundary before each consequential action (not just screening inputs or reviewing completed runs), and tracing what data source and influence shaped each element of a pending action can reliably distinguish attacker-manipulated steps from benign ones.
Building and Evaluating Search Agents Beyond Benchmarks
Hamel Husain · youtube · ~13 min
When evaluating search agents, don't rely solely on top-line accuracy; also measure efficiency signals like number of search calls and query redundancy to understand whether performance comes from a strong retriever, a strong model, or wasteful trial-and-error.
Capability-Driven Self-Evolution of Agent Memory (PrisMem)
article · ~1 min
When iteratively improving AI agent memory via feedback, decomposing optimization into distinct capability dimensions (rather than relying on aggregate performance) prevents masking of localized improvements and enables more targeted, effective self-evolution.
Context Libraries for AI Agent Management
article · ~10 min
Context libraries require ongoing maintenance just like code—treat them as living infrastructure that needs periodic review, not one-time setup, and consider letting the agent itself update context to reduce decay over time.
Self-Correcting Memory via Claim-Evidence Linking in OpenWiki
article · ~7 min
Long-term agent memory systems should persist not just beliefs but the evidence behind them, since comparing evidence versions over time is a lightweight way to detect staleness and trigger self-correction without needing separate status tracking.
Error Analysis Without Direct Data Access
Hamel Husain · youtube · ~2 min
Follow a hierarchy when data access is restricted—real data (even limited) > domain expert review > redacted traces > synthetic data as last resort—since each step down loses fidelity in catching actual model failures.
Binary Pass/Fail Scoring for LLM Judges
Hamel Husain · youtube · ~1 min
Avoid continuous or Likert-scale scoring in LLM evaluation pipelines since it obscures decision-making; force a binary pass/fail judgment tied directly to a real decision like shipping a feature.
Don't Build Agents, Build Environments Instead
Hamel Husain · youtube · ~14 min
When scaling agentic workflows, invest engineering effort in designing reliable, reproducible environments/sandboxes (the infrastructure agents run in) rather than continuing to tweak agent logic, since most agent frameworks already share nearly identical core tooling.
AI Roll-Ups: Buying Small Businesses with AI Agents
Greg Isenberg · youtube · ~11 min
The convergence of retiring owners with no succession plan, AI finally being capable enough to handle repetitive back-office work (data entry, status updates, document chasing), and widespread underpricing of this opportunity creates a window for solo founders to acquire cash-flowing small businesses and dramatically improve margins using AI agents instead of raising large funds.
Software Factory: AI Agent Development Workflow System
Greg Isenberg · youtube · ~14 min
Instead of relying on ad-hoc prompting in tools like Cursor or Claude Code, codify your development workflow, skills, and domain knowledge into a reusable markdown-based system so you can run multiple AI agents in parallel while trusting the output quality.
Software Factory: Harness-Agnostic Agentic Development Workflow
Ras Mic · youtube · ~14 min
Instead of relying on a specific AI tool or model, invest in building portable, well-documented skills and workflows (like an agents.md file) that can be transferred across any agentic harness, making your development speed independent of any single platform.
Agentic Operating Level Framework for Engineers
IndyDevDan · youtube · ~14 min
Effective engineers deliberately choose their operating level (control vs. leverage) based on the task at hand, rather than assuming higher-level, more automated approaches are always better.
Shared Company Agent vs. Personal AI Agents
Laura Entis · article · ~8 min
When deploying AI agents in a team setting, favor one shared, continuously-learning company agent over many individual personal bots to avoid maintenance overhead and fragmented knowledge; reserve personal agents for transactional vs. relationship-based tasks at an individual level.
Forward Deployed Engineering: Monetizing AI Agent Deployment
Greg Isenberg · youtube · ~12 min
Forward deployed engineers capture outsized compensation by taking a percentage of the measurable value they create (e.g., $5-25M in efficiency or revenue gains) through AI agent deployment, rather than charging flat fees for implementation work.
Every Agent: Slack-Based AI Coworker Tool
Dan Shipper · article · ~8 min
Prompting in public (working with AI visibly in shared channels rather than private chats) helps AI workflows spread organically across a team, since coworkers can see and replicate successful prompts/skills instead of just the finished output.