💡 Analysis on LLM Wiki — Agentic AI Landscapehttps://blog.imfsoftware.com/llm-wiki/docs/analysis/Recent content in 💡 Analysis on LLM Wiki — Agentic AI LandscapeHugoen-us<link>https://blog.imfsoftware.com/llm-wiki/docs/analysis/beyond-code-industry-impact/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.imfsoftware.com/llm-wiki/docs/analysis/beyond-code-industry-impact/</guid><description><h1 id="agentic-ai-beyond-software-six-industries-being-transformed">Agentic AI Beyond Software: Six Industries Being Transformed<a class="anchor" href="#agentic-ai-beyond-software-six-industries-being-transformed">#</a></h1> <p>Synthesized from 5 sources across this wiki. This analysis tests whether the patterns identified in the wiki&rsquo;s software-focused sources generalize to other domains — and what that means for the broader landscape.</p> <hr> <h2 id="the-question">The Question<a class="anchor" href="#the-question">#</a></h2> <p>This wiki was built from software development tools and frameworks. The <a href="https://blog.imfsoftware.com/llm-wiki/docs/analysis/cross-source-themes/">cross-source-themes</a> analysis noted: &ldquo;Almost everything is optimized for software development. The <a href="https://blog.imfsoftware.com/llm-wiki/docs/concepts/llm-wiki-pattern/">llm-wiki-pattern</a> and <a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/pai/">pai</a> gesture toward broader applications, but tooling lags.&rdquo;</p></description></item><item><title/><link>https://blog.imfsoftware.com/llm-wiki/docs/analysis/cost-optimization-guide/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.imfsoftware.com/llm-wiki/docs/analysis/cost-optimization-guide/</guid><description><h1 id="why-your-ai-agent-costs-10-more-than-it-should">Why Your AI Agent Costs 10× More Than It Should<a class="anchor" href="#why-your-ai-agent-costs-10-more-than-it-should">#</a></h1> <p>Synthesized from 6 sources across this wiki. This analysis connects token economics to architectural decisions — showing that cost optimization is not a billing concern but a design concern. Every architecture choice in this wiki (memory, multi-agent, context management) has a direct cost implication.</p> <hr> <h2 id="the-reality-check">The Reality Check<a class="anchor" href="#the-reality-check">#</a></h2> <p>Most developers don&rsquo;t have a useful mental model for agent token consumption. Time is not the variable. Tokens are. An agent doing a 10-second task can use a million tokens inefficiently. An agent taking 5 minutes on a complex task can use 50,000 tokens if well-structured. Same time. 20× different cost.</p></description></item><item><title/><link>https://blog.imfsoftware.com/llm-wiki/docs/analysis/cross-source-themes/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.imfsoftware.com/llm-wiki/docs/analysis/cross-source-themes/</guid><description><h1 id="cross-source-theme-analysis">Cross-Source Theme Analysis<a class="anchor" href="#cross-source-theme-analysis">#</a></h1> <p>42 sources, 12 tools, 4 OSS frameworks, 4 benchmarks, 2 standards, 3 methodologies, 4 memory systems, and sources covering cost, governance, UX, and industry impact. Here are the themes that appear across 3+ sources independently.</p> <blockquote class='book-hint '> <p><strong>Refresh history</strong>: Originally written against 11 sources (Apr 9). Refreshed Apr 15 against 33 sources. Refreshed May 9 against 42 sources — added Gas Town, Symphony, Multica evidence to existing themes.</p></description></item><item><title/><link>https://blog.imfsoftware.com/llm-wiki/docs/analysis/getting-started-guide/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.imfsoftware.com/llm-wiki/docs/analysis/getting-started-guide/</guid><description><h1 id="building-your-first-long-running-agent-a-getting-started-guide">Building Your First Long-Running Agent: A Getting Started Guide<a class="anchor" href="#building-your-first-long-running-agent-a-getting-started-guide">#</a></h1> <p>Synthesized from 8 wiki analyses and sources. This guide bridges the gap between the wiki&rsquo;s architectural recommendations and hands-on implementation. It answers: &ldquo;I&rsquo;ve read the comparisons — now what do I actually build?&rdquo;</p> <hr> <h2 id="the-architecture-the-wiki-recommends">The Architecture the Wiki Recommends<a class="anchor" href="#the-architecture-the-wiki-recommends">#</a></h2> <p>Based on all 37 sources, the wiki converges on this stack for a long-running agent:</p> <table> <thead> <tr> <th>Layer</th> <th>Choice</th> <th>Why</th> </tr> </thead> <tbody> <tr> <td><strong>Orchestration</strong></td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/langgraph-agent-orchestration/">langgraph-agent-orchestration</a></td> <td>Checkpointing, human-in-the-loop, most production-ready OSS</td> </tr> <tr> <td><strong>Memory</strong></td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/mem0/">mem0</a> (Graph + Vector)</td> <td>Benchmarked (93% token reduction), handles relationships and forgetting</td> </tr> <tr> <td><strong>Memory Lifecycle</strong></td> <td>Decay + contradiction + confidence</td> <td>Prevents drift, keeps store coherent (<a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/memory-lifecycle-drift/">memory-lifecycle-drift</a>)</td> </tr> <tr> <td><strong>Cost Control</strong></td> <td>Prompt caching + model routing + session discipline</td> <td>60-80% savings (<a href="https://blog.imfsoftware.com/llm-wiki/docs/analysis/cost-optimization-guide/">cost-optimization-guide</a>)</td> </tr> <tr> <td><strong>Trust</strong></td> <td>Autonomy Dial + Action Audit</td> <td>Start at &ldquo;Plan &amp; Propose,&rdquo; graduate to autonomous (<a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/agentic-ux-patterns/">agentic-ux-patterns</a>)</td> </tr> <tr> <td><strong>Observability</strong></td> <td>OpenTelemetry spans</td> <td>Trace every agent action, debug failures (<a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/multi-agent-observability/">multi-agent-observability</a>)</td> </tr> </tbody> </table> <hr> <h2 id="phase-1-single-agent-with-memory-week-1">Phase 1: Single Agent with Memory (Week 1)<a class="anchor" href="#phase-1-single-agent-with-memory-week-1">#</a></h2> <p>Start with one agent, not a multi-agent system. Get memory working first.</p></description></item><item><title/><link>https://blog.imfsoftware.com/llm-wiki/docs/analysis/governance-safety-overview/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.imfsoftware.com/llm-wiki/docs/analysis/governance-safety-overview/</guid><description><h1 id="the-shadow-ai-problem-governing-agents-you-dont-know-about">The Shadow AI Problem: Governing Agents You Don&rsquo;t Know About<a class="anchor" href="#the-shadow-ai-problem-governing-agents-you-dont-know-about">#</a></h1> <p>Synthesized from 6 sources across this wiki. This analysis connects the governance gap to the wiki&rsquo;s existing security models, maps the regulatory landscape, and shows how UX patterns are the user-facing layer of governance.</p> <hr> <h2 id="the-scale-of-the-problem">The Scale of the Problem<a class="anchor" href="#the-scale-of-the-problem">#</a></h2> <p>The numbers are stark (<a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/agentic-ai-governance/">agentic-ai-governance</a>):</p> <ul> <li><strong>40%</strong> of enterprise apps will integrate AI agents by end of 2026 (up from &lt;5% in 2025)</li> <li><strong>68%</strong> of employees use AI tools without IT approval</li> <li><strong>80%</strong> of organizations have experienced risky AI agent behaviors</li> <li><strong>$412K/year</strong> average cost of Shadow AI</li> <li><strong>$670K</strong> higher breach costs for enterprises with 65%+ ungoverned AI tools</li> </ul> <p>This isn&rsquo;t a future risk. It&rsquo;s a current operational reality.</p></description></item><item><title/><link>https://blog.imfsoftware.com/llm-wiki/docs/analysis/how-to-eval-a-skill/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.imfsoftware.com/llm-wiki/docs/analysis/how-to-eval-a-skill/</guid><description><h1 id="how-to-eval-a-skill-practical-guide">How to Eval a Skill (Practical Guide)<a class="anchor" href="#how-to-eval-a-skill-practical-guide">#</a></h1> <p>Anthropic&rsquo;s prompt evals measure whether a prompt produces good output. Skill evals are harder because a skill has more surface area: it needs to trigger correctly, execute the right steps, use the right tools, produce the right output, and NOT trigger on the wrong inputs.</p> <p>This guide maps Anthropic&rsquo;s eval methodology onto skills, drawing from the wiki&rsquo;s sources.</p> <hr> <h2 id="the-key-difference-prompts-vs-skills">The Key Difference: Prompts vs. Skills<a class="anchor" href="#the-key-difference-prompts-vs-skills">#</a></h2> <table> <thead> <tr> <th></th> <th>Prompt Eval</th> <th>Skill Eval</th> </tr> </thead> <tbody> <tr> <td><strong>What you test</strong></td> <td>Does this prompt produce good output?</td> <td>Does this skill trigger, execute, and produce correctly?</td> </tr> <tr> <td><strong>Input</strong></td> <td>A prompt + expected output</td> <td>A prompt + context + expected behavior chain</td> </tr> <tr> <td><strong>Failure modes</strong></td> <td>Bad output</td> <td>Wrong trigger, wrong steps, wrong tools, bad output, false positive activation</td> </tr> <tr> <td><strong>Non-determinism</strong></td> <td>Output varies</td> <td>Trigger, routing, tool selection, AND output all vary</td> </tr> </tbody> </table> <p>A skill eval must test the full chain: <strong>routing → activation → execution → output → side effects</strong>.</p></description></item><item><title/><link>https://blog.imfsoftware.com/llm-wiki/docs/analysis/hybrid-memory-architecture/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.imfsoftware.com/llm-wiki/docs/analysis/hybrid-memory-architecture/</guid><description><h1 id="hybrid-memory-architecture-combining-wiki--mem0">Hybrid Memory Architecture: Combining Wiki + Mem0<a class="anchor" href="#hybrid-memory-architecture-combining-wiki--mem0">#</a></h1> <p>A novel architecture combining the File+Database pattern (this wiki) with Graph+Vector retrieval (<a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/mem0/">mem0</a>). Neither pattern alone achieves full CMA compliance — together they reach 5/6, matching <a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/pai/">pai</a>&rsquo;s hierarchical approach but with full transparency.</p> <hr> <h2 id="why-neither-pattern-is-enough-alone">Why Neither Pattern Is Enough Alone<a class="anchor" href="#why-neither-pattern-is-enough-alone">#</a></h2> <table> <thead> <tr> <th>Capability</th> <th>Wiki (File+DB)</th> <th>Mem0 (Graph+Vector)</th> </tr> </thead> <tbody> <tr> <td>Fast retrieval (&lt;100ms)</td> <td>❌</td> <td>✅</td> </tr> <tr> <td>Deep synthesis</td> <td>✅</td> <td>❌</td> </tr> <tr> <td>Human-readable</td> <td>✅</td> <td>❌</td> </tr> <tr> <td>Automatic forgetting</td> <td>❌</td> <td>✅</td> </tr> <tr> <td>Contradiction detection</td> <td>Manual (lint)</td> <td>Automatic (write-time)</td> </tr> <tr> <td>Relationship queries</td> <td>Via wikilinks (manual)</td> <td>Graph traversal (automatic)</td> </tr> <tr> <td>Git version history</td> <td>✅</td> <td>❌</td> </tr> <tr> <td>CMA compliance</td> <td>2/6</td> <td>4/6</td> </tr> </tbody> </table> <p>The wiki excels at depth, transparency, and synthesis. Mem0 excels at speed, automation, and relationships. They&rsquo;re complementary.</p></description></item><item><title/><link>https://blog.imfsoftware.com/llm-wiki/docs/analysis/key-insights-agentic-landscape/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.imfsoftware.com/llm-wiki/docs/analysis/key-insights-agentic-landscape/</guid><description><h1 id="key-insights-the-agentic-ai-landscape-may-2026">Key Insights: The Agentic AI Landscape (May 2026)<a class="anchor" href="#key-insights-the-agentic-ai-landscape-may-2026">#</a></h1> <p>Synthesized from 42 sources across this wiki. This analysis captures the patterns, tensions, and emerging consensus visible when you look across the entire landscape.</p> <blockquote class='book-hint '> <p><strong>Refresh history</strong>: Originally written against 16 sources (Apr 8-10). Refreshed Apr 15 against 33 sources. Refreshed May 9 against 42 sources — added Gas Town, Symphony, Multica; updated layers, multi-agent philosophies, and &ldquo;What&rsquo;s Still Missing.&rdquo;</p> </blockquote><hr> <h2 id="1-seven-layers-have-emerged-was-six">1. Seven Layers Have Emerged (was Six)<a class="anchor" href="#1-seven-layers-have-emerged-was-six">#</a></h2> <table> <thead> <tr> <th>Layer</th> <th>Representatives</th> <th>Core Bet</th> </tr> </thead> <tbody> <tr> <td><strong>Company</strong></td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/paperclip/">paperclip</a></td> <td>Org charts, budgets, governance, goal alignment</td> </tr> <tr> <td><strong>Methodology</strong></td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/spec-kit/">spec-kit</a>, <a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/bmad-method/">bmad-method</a></td> <td>Specs, plans, tasks, quality gates</td> </tr> <tr> <td><strong>Orchestration</strong></td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/gastown/">gastown</a>, <a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/symphony/">symphony</a>, <a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/multica/">multica</a></td> <td>Workspace coordination, issue-to-agent automation, team collaboration</td> </tr> <tr> <td><strong>Infrastructure</strong></td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/scion/">scion</a>, <a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/langgraph-agent-orchestration/">langgraph-agent-orchestration</a>, <a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/autogen-multi-agent/">autogen-multi-agent</a></td> <td>Containers, runtimes, graph orchestration</td> </tr> <tr> <td><strong>Tool</strong></td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/claude-code/">claude-code</a>, <a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/kiro/">kiro</a>, <a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/crewai-multi-agent/">crewai-multi-agent</a></td> <td>Agentic loop, skills, hooks, MCP</td> </tr> <tr> <td><strong>Pattern</strong></td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/fabric/">fabric</a>, <a href="https://blog.imfsoftware.com/llm-wiki/docs/concepts/agent-skills-standard/">agent-skills-standard</a>, <a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/openai-swarm/">openai-swarm</a></td> <td>Curated prompts, composable strategies, handoffs</td> </tr> <tr> <td><strong>Memory</strong></td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/mem0/">mem0</a>, <a href="https://blog.imfsoftware.com/llm-wiki/docs/concepts/agent-memory-persistence/">agent-memory-persistence</a></td> <td>Persistence, retrieval, forgetting, knowledge graphs</td> </tr> </tbody> </table> <p><strong>New (May 2026)</strong>: Orchestration is now its own layer between Methodology and Infrastructure. Gas Town (workspace/process-model), Symphony (spec/scheduler), and Multica (platform/teammates) represent three distinct approaches at this layer. The emerging stack: Paperclip → Multica → Gas Town → LangGraph/MAF → Claude Code/Kiro → Scion → Mem0.</p></description></item><item><title/><link>https://blog.imfsoftware.com/llm-wiki/docs/analysis/memory-architecture-comparison/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.imfsoftware.com/llm-wiki/docs/analysis/memory-architecture-comparison/</guid><description><h1 id="how-ai-agents-remember-a-comparison-of-memory-architectures">How AI Agents Remember: A Comparison of Memory Architectures<a class="anchor" href="#how-ai-agents-remember-a-comparison-of-memory-architectures">#</a></h1> <p>Synthesized from 8 sources across this wiki. This analysis compares the four dominant memory architecture patterns, maps them against formal requirements from cognitive science, and provides a practical decision framework for choosing the right approach.</p> <hr> <h2 id="the-core-problem">The Core Problem<a class="anchor" href="#the-core-problem">#</a></h2> <p>LLMs are stateless. Every session starts blank. Without persistent memory, agents re-discover known information, repeat mistakes, and can&rsquo;t build on prior work. But persistent memory introduces its own risks: stale facts, compounding errors, and growing retrieval noise. This is the fundamental tension the <a href="https://blog.imfsoftware.com/llm-wiki/docs/analysis/cross-source-themes/">cross-source-themes</a> identified as &ldquo;the unsolved frontier.&rdquo;</p></description></item><item><title/><link>https://blog.imfsoftware.com/llm-wiki/docs/analysis/multi-agent-framework-guide/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.imfsoftware.com/llm-wiki/docs/analysis/multi-agent-framework-guide/</guid><description><h1 id="choosing-a-multi-agent-framework-in-2026">Choosing a Multi-Agent Framework in 2026<a class="anchor" href="#choosing-a-multi-agent-framework-in-2026">#</a></h1> <p>Synthesized from 14 sources across this wiki (updated May 2026). This analysis compares multi-agent frameworks and orchestration tools, maps their coordination philosophies, and provides a practical decision framework.</p> <hr> <h2 id="the-landscape-three-tiers">The Landscape: Three Tiers<a class="anchor" href="#the-landscape-three-tiers">#</a></h2> <p>The wiki now covers fourteen distinct approaches to multi-agent orchestration, split across three tiers:</p> <p><strong>Product-level</strong> (opinionated, integrated):</p> <table> <thead> <tr> <th>Tool</th> <th>Philosophy</th> <th>Layer</th> </tr> </thead> <tbody> <tr> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/scion/">scion</a></td> <td>Infrastructure-first. Hypervisor for agents. Container isolation.</td> <td>Infrastructure</td> </tr> <tr> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/kiro/">kiro</a></td> <td>Product-first. Frontier agent. Hours of autonomy. PR output.</td> <td>Tool</td> </tr> <tr> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/claude-code/">claude-code</a></td> <td>Tool-first. Subagents, MCP, skills, permission modes.</td> <td>Tool</td> </tr> <tr> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/paperclip/">paperclip</a></td> <td>Company-first. Org charts, budgets, governance above agents.</td> <td>Company</td> </tr> </tbody> </table> <p><strong>Orchestration tools</strong> (workspace/platform-level, agent-agnostic):</p></description></item><item><title/><link>https://blog.imfsoftware.com/llm-wiki/docs/analysis/ten-pillars-evidence-map/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.imfsoftware.com/llm-wiki/docs/analysis/ten-pillars-evidence-map/</guid><description><h1 id="evidence-map-supporting-the-ten-pillars-framework">Evidence Map: Supporting the Ten Pillars Framework<a class="anchor" href="#evidence-map-supporting-the-ten-pillars-framework">#</a></h1> <p>This analysis maps each pillar from <a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/ten-pillars-agentic-skill-design/">ten-pillars-agentic-skill-design</a> against real-world evidence collected across 11 sources in this wiki. Your paper acknowledged &ldquo;no original controlled study&rdquo; as a limitation — the wiki now provides post-hoc validation from production implementations.</p> <hr> <h2 id="pillar-1-architecture-and-structure">Pillar 1: Architecture and Structure<a class="anchor" href="#pillar-1-architecture-and-structure">#</a></h2> <p><strong>Your claim</strong>: Organize content into clearly defined sections — metadata, interfaces, core logic, workflows, configuration.</p> <p><strong>Supporting evidence</strong>:</p> <ul> <li><strong><a href="https://blog.imfsoftware.com/llm-wiki/docs/concepts/agent-skills-standard/">agent-skills-standard</a></strong> codified this into a formal spec: SKILL.md with YAML frontmatter (name, description, license, compatibility, metadata, allowed-tools) + markdown body + optional scripts/references/assets directories. This is now an open standard at agentskills.io.</li> <li><strong><a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/claude-code/">claude-code</a></strong> implements it: <code>.claude</code> directory with CLAUDE.md, <code>.claude/rules/</code>, <code>.claude/skills/</code>, <code>.claude/agents/</code>. Hierarchical, scoped (org → project → user → local).</li> <li><strong><a href="https://blog.imfsoftware.com/llm-wiki/docs/entities/pai/">pai</a></strong> takes it further: USER/ vs SYSTEM/ separation. Six layers of customization (identity, preferences, workflows, skills, hooks, memory). Upgrade-safe architecture.</li> <li><strong><a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/skills-pipeline-sleestk/">skills-pipeline-sleestk</a></strong> follows the spec exactly: each skill is a directory with SKILL.md + references/ subdirectory.</li> </ul> <p><strong>Strength</strong>: Strong. Multiple independent implementations converged on the same structure. The Agent Skills spec formalizes what your paper recommended.</p></description></item><item><title/><link>https://blog.imfsoftware.com/llm-wiki/docs/analysis/wiki-gap-analysis-apr-2026/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.imfsoftware.com/llm-wiki/docs/analysis/wiki-gap-analysis-apr-2026/</guid><description><h1 id="wiki-gap-analysis-status-tracker-april-2026">Wiki Gap Analysis: Status Tracker (April 2026)<a class="anchor" href="#wiki-gap-analysis-status-tracker-april-2026">#</a></h1> <p>Generated from 6 conversational query tests against the wiki. 20 gaps identified, 15 addressed, 5 remaining.</p> <h2 id="addressed-15-">Addressed (15 ✅)<a class="anchor" href="#addressed-15-">#</a></h2> <table> <thead> <tr> <th>#</th> <th>Gap</th> <th>How Addressed</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Getting started tutorial</td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/analysis/getting-started-guide/">getting-started-guide</a> — 5-phase roadmap from single agent to multi-agent</td> </tr> <tr> <td>2</td> <td>LangGraph + Mem0 integration</td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/langgraph-mem0-integration/">langgraph-mem0-integration</a> — DigitalOcean step-by-step tutorial</td> </tr> <tr> <td>5</td> <td>Agent observability tooling</td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/multi-agent-observability/">multi-agent-observability</a> — OpenTelemetry spans, debugging patterns</td> </tr> <tr> <td>6</td> <td>Memory quality metrics</td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/memory-lifecycle-drift/">memory-lifecycle-drift</a> — decay scoring, confidence, contradiction detection</td> </tr> <tr> <td>7</td> <td>Memory regression testing</td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/memory-lifecycle-drift/">memory-lifecycle-drift</a> — lifecycle components as automated maintenance</td> </tr> <tr> <td>9</td> <td>CrewAI practical examples</td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/crewai-production-guide/">crewai-production-guide</a> — content pipeline, customer support, event-driven patterns</td> </tr> <tr> <td>10</td> <td>Multi-agent debugging</td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/multi-agent-observability/">multi-agent-observability</a> — trace hierarchy, span-level failure diagnosis</td> </tr> <tr> <td>11</td> <td>Agent role design patterns</td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/crewai-production-guide/">crewai-production-guide</a> — role+backstory patterns, per-agent model selection</td> </tr> <tr> <td>12</td> <td>Environmental impact data</td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/ai-environmental-impact/">ai-environmental-impact</a> — 30 models benchmarked, energy/carbon/water per query</td> </tr> <tr> <td>13</td> <td>Sustainable AI practices</td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/ai-environmental-impact/">ai-environmental-impact</a> — eco-efficiency rankings, batching strategies, Jevons Paradox</td> </tr> <tr> <td>14</td> <td>Multi-agent memory conflict</td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/shared-agent-memory/">shared-agent-memory</a> — implicit resolution via recency; explicit conflict confirmed as open problem</td> </tr> <tr> <td>15</td> <td>CRDTs / shared state</td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/shared-agent-memory/">shared-agent-memory</a> — shared solution store pattern (store + write hook + retrieval + threshold)</td> </tr> <tr> <td>16</td> <td>Agent consensus mechanisms</td> <td><a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/shared-agent-memory/">shared-agent-memory</a> — implicit via similarity threshold; explicit consensus unsolved</td> </tr> </tbody> </table> <h2 id="partially-addressed-2-">Partially Addressed (2 🟡)<a class="anchor" href="#partially-addressed-2-">#</a></h2> <table> <thead> <tr> <th>#</th> <th>Gap</th> <th>Status</th> </tr> </thead> <tbody> <tr> <td>8</td> <td>Memory debugging tools</td> <td>Covered by <a href="https://blog.imfsoftware.com/llm-wiki/docs/sources/multi-agent-observability/">multi-agent-observability</a> (span-level tracing) but no memory-specific debugging source</td> </tr> <tr> <td>20</td> <td>Memory debugging tools</td> <td>Same as #8</td> </tr> </tbody> </table> <h2 id="open-5-">Open (5 ⬜)<a class="anchor" href="#open-5-">#</a></h2> <table> <thead> <tr> <th>#</th> <th>Gap</th> <th>Category</th> <th>Next Action</th> </tr> </thead> <tbody> <tr> <td>3</td> <td>Team knowledge management agents</td> <td>Non-Code</td> <td>Research: sources on AI agents for team wikis, shared knowledge bases</td> </tr> <tr> <td>4</td> <td>Knowledge quality eval metrics</td> <td>Eval</td> <td>Research/Create: metrics for cross-reference accuracy, entity extraction completeness, freshness</td> </tr> <tr> <td>17</td> <td>Content creation workflow (analysis → blog)</td> <td>Wiki Pattern</td> <td>Create: define workflow for turning wiki analyses into publishable content</td> </tr> <tr> <td>18</td> <td>AI-assisted writing/editing for prose</td> <td>Non-Code</td> <td>Research: sources on using agents for drafting, editing, refining articles</td> </tr> <tr> <td>19</td> <td>Wiki &ldquo;publish&rdquo; operation</td> <td>Wiki Pattern</td> <td>Create: add publish workflow to AGENTS.md alongside ingest/query/lint</td> </tr> </tbody> </table> <h2 id="remaining-gaps-cluster-into-two-groups">Remaining Gaps Cluster Into Two Groups<a class="anchor" href="#remaining-gaps-cluster-into-two-groups">#</a></h2> <p><strong>Content Pipeline (#17, 18, 19)</strong>: How to turn wiki knowledge into blog posts, articles, and teaching materials. This is the user&rsquo;s stated 6-month goal. Addressing these would complete the wiki&rsquo;s lifecycle from ingest → query → analyze → publish.</p></description></item><item><title/><link>https://blog.imfsoftware.com/llm-wiki/docs/analysis/wiki-gap-analysis-may-2026/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.imfsoftware.com/llm-wiki/docs/analysis/wiki-gap-analysis-may-2026/</guid><description><h1 id="wiki-gap-analysis-status-tracker-may-2026">Wiki Gap Analysis: Status Tracker (May 2026)<a class="anchor" href="#wiki-gap-analysis-status-tracker-may-2026">#</a></h1> <p>Monthly health check. Assesses progress since April, identifies new gaps from recent ingests, and tracks open items.</p> <h2 id="wiki-stats">Wiki Stats<a class="anchor" href="#wiki-stats">#</a></h2> <table> <thead> <tr> <th>Metric</th> <th>April 15</th> <th>May 9</th> <th>Delta</th> </tr> </thead> <tbody> <tr> <td>Sources</td> <td>39</td> <td>42</td> <td>+3</td> </tr> <tr> <td>Entities</td> <td>17</td> <td>20</td> <td>+3</td> </tr> <tr> <td>Concepts</td> <td>20</td> <td>20</td> <td>—</td> </tr> <tr> <td>Analyses</td> <td>12</td> <td>13</td> <td>+1</td> </tr> <tr> <td>Themes</td> <td>12</td> <td>12</td> <td>—</td> </tr> <tr> <td>Multi-agent orchestration sources</td> <td>7</td> <td>10</td> <td>+3</td> </tr> </tbody> </table> <h2 id="may-activity">May Activity<a class="anchor" href="#may-activity">#</a></h2> <p>3 ingests, 1 analysis:</p> <ul> <li><strong>Gas Town</strong> — workspace-first orchestration (git worktrees, merge queue, federation)</li> <li><strong>Symphony</strong> — OpenAI&rsquo;s spec-first orchestration (WORKFLOW.md, per-issue workspaces)</li> <li><strong>Multica</strong> — platform-first orchestration (agents as teammates, compounding skills)</li> <li><strong>Orchestration Tools Compared</strong> — analysis synthesizing 3 external comparison sources</li> </ul> <h2 id="april-open-gaps-status-update">April Open Gaps: Status Update<a class="anchor" href="#april-open-gaps-status-update">#</a></h2> <table> <thead> <tr> <th>#</th> <th>Gap</th> <th>April Status</th> <th>May Status</th> <th>Notes</th> </tr> </thead> <tbody> <tr> <td>3</td> <td>Team knowledge management agents</td> <td>⬜ Open</td> <td>🟡 Partial</td> <td>Multica&rsquo;s &ldquo;agents as teammates&rdquo; + compounding skills partially addresses this</td> </tr> <tr> <td>4</td> <td>Knowledge quality eval metrics</td> <td>⬜ Open</td> <td>⬜ Open</td> <td>No new sources</td> </tr> <tr> <td>17</td> <td>Content creation workflow (analysis → blog)</td> <td>⬜ Open</td> <td>⬜ Open</td> <td>No progress</td> </tr> <tr> <td>18</td> <td>AI-assisted writing/editing for prose</td> <td>⬜ Open</td> <td>⬜ Open</td> <td>No progress</td> </tr> <tr> <td>19</td> <td>Wiki &ldquo;publish&rdquo; operation</td> <td>⬜ Open</td> <td>⬜ Open</td> <td>No progress</td> </tr> </tbody> </table> <p><strong>Summary</strong>: 0 of 5 April gaps fully closed. Content pipeline gaps (#17-19) remain the highest priority — they directly support the 6-month goal of turning wiki knowledge into publishable content.</p></description></item></channel></rss>