# Swarm Signal

> Independent AI publication covering agents, multi-agent systems, and the engineering challenges of deploying autonomous AI. 103 articles grounded in arxiv papers, technical reports, and production case studies. Published by Tyler Casey.

This file highlights 27 key articles from 103 total (22 Guides + 81 Signals). For the complete article content, see [llms-full.txt](https://swarmsignal.net/llms-full.txt). For citable statistics and findings, see [llms-ctx.txt](https://swarmsignal.net/llms-ctx.txt).

## Agent Design

Architecture, tool use, orchestration patterns, and failure modes for production AI agents.

- [Computer-Use Agents Can't Stop Breaking Things](https://swarmsignal.net/computer-use-agents-ai-browser-automation-anthropic-computer/): Examines computer-use agent failure modes: unauthorized file deletion, GUI prompt injection, and the trust gap between demos and production.
- [The Control Interface Problem in Physical AI](https://swarmsignal.net/physical-ai-and-embodied-agents-2026-humanoid-robots-vision/): Examines the control interface problem in physical AI. Covers NVIDIA Cosmos, VLA training gaps, spatial reasoning failures, and humanoid robots.
- [Knowledge Graphs Just Made RAG Worth the Complexity](https://swarmsignal.net/graphrag-knowledge-graphs-combined-with-retrieval-augmented/): Evaluates GraphRAG architectures combining knowledge graphs with RAG. Analyzes multi-hop reasoning improvements and implementation cost trade-offs.
- [The Observability Gap in Production AI Agents](https://swarmsignal.net/ai-agent-observability-and-monitoring-in-production-distribu/): Evaluates the observability gap in production AI agents. Covers reasoning vs execution anomalies, distributed tracing, and audit systems.
- [Function Calling Is the Interface AI Research Forgot](https://swarmsignal.net/function-calling-and-tool-use-in-llms-how-ai-agents-interact/): Examines the gap between function calling capability and production tool use reliability. Covers parameter extraction, RC-GRPO, and multilingual failures.
- [AI Agents Are Security's Newest Nightmare](https://swarmsignal.net/prompt-injection-attacks-on-ai-agents-indirect-prompt-inject/): Evaluates prompt injection attack vectors against AI agents. Covers MUZZLE benchmark (92% hijack rate), CausalArmor defenses, and mitigation.
- [When AI Agents Have Tools, They Lie More](https://swarmsignal.net/ai-agent-hallucinations-why-agents-hallucinate-with-tool-acc/): Examines why tool-augmented AI agents hallucinate more than standard chatbots. Covers trajectory drift, fabricated tool outputs, and mitigation.
- [Why Agent Builders Are Betting on 7B Models Over GPT-4](https://swarmsignal.net/small-language-models-slms-vs-llms-for-ai-agents-efficient-o/): Compares small language models vs frontier LLMs for agent tasks. Analyzes cost-performance with Gemma 2 9B, Phi-3-mini, and quantized models.
- [When Your Judge Can't Read the Room](https://swarmsignal.net/llm-as-judge-ai-evaluation-at-scale-pointwise-scoring-pairwi/): Evaluates LLM-as-Judge systems for AI evaluation at scale. Covers pointwise scoring, pairwise comparison, position bias, and accuracy limits.
- [Types of AI Agents: Reactive, Deliberative, Hybrid, and What Comes Next](https://swarmsignal.net/types-of-ai-agents/): Classifies AI agents into four types (reactive, deliberative, hybrid, autonomous) with production tradeoffs and real-world deployment examples.
- [How to Test and Debug AI Agents](https://swarmsignal.net/testing-debugging-ai-agents/): Guide to testing AI agents covering the MAST failure taxonomy (14 modes), CLEAR consistency framework, and a production testing pipeline.
- [From Prompt to Partner: A Practical Guide to Building Your First AI Agent](https://swarmsignal.net/from-prompt-to-partner-a-practical-guide-to-building-your-first-ai-agent/): Practical guide to building production AI agents. Covers the three pillars (model, tools, instructions), ReAct framework, and deployment patterns.

## Swarm Systems

Multi-agent coordination, swarm intelligence, communication protocols, and collective behavior.

- [Agents That Reshape, Audit, and Trade With Each Other](https://swarmsignal.net/agents-that-reshape-audit-and-trade-with-each-other/): Analyzes emerging multi-agent behaviors: dynamic topology reconfiguration, self-auditing deception detection, and economic negotiation.
- [AI Agent Orchestration Patterns: From Single Agent to Production Swarms](https://swarmsignal.net/ai-agent-orchestration-patterns/): Taxonomy of six production agent orchestration patterns with framework implementations (CrewAI, LangGraph, AutoGen, OpenAI Agents SDK).
- [Swarm Intelligence Explained: From Ant Colonies to AI Agent Fleets](https://swarmsignal.net/swarm-intelligence-explained/): Traces swarm intelligence from ant colonies through classical algorithms (ACO, PSO) to modern LLM agent swarms and production applications.
- [Multi-Agent Systems Explained: How AI Agents Coordinate, Compete, and Fail](https://swarmsignal.net/multi-agent-systems-explained/): Guide to multi-agent systems covering coordination patterns, competition dynamics, failure modes, and production orchestration strategies.

## Reasoning & Memory

RAG architectures, agent memory, context engineering, inference-time compute, and reasoning tokens.

- [From Goldfish to Elephant: How Agent Memory Finally Got an Architecture](https://swarmsignal.net/agent-memory-architecture-guide/): Guide to agent memory architectures covering tiered memory (MemGPT), budget-aware allocation, and temporal knowledge graphs.
- [From Answer to Insight: Why Reasoning Tokens Are a Quiet Revolution in AI](https://swarmsignal.net/from-answer-to-insight-why-reasoning-tokens-are-a-quiet-revolution-in-ai/): Explains reasoning tokens (hidden chain-of-thought) comparing OpenAI o1, DeepSeek R1, and Claude extended thinking with cost-performance data.
- [The Goldfish Brain Problem: Why AI Agents Forget and How to Fix It](https://swarmsignal.net/the-goldfish-brain-problem-why-ai-agents-forget-and-how-to-fix-it/): Guide to the AI agent memory problem. Covers Stanford's generative agents, MemGPT's tiered architecture, and three policy approaches to persistence.

## Safety & Governance

AI safety reports, alignment, bias, regulation (EU AI Act), benchmarks, and red teaming.

- [AI Guardrails for Agents: How to Build Safe, Validated LLM Systems](https://swarmsignal.net/ai-guardrails-agents/): Compares four guardrail systems (NeMo, Guardrails AI, Bedrock, Llama Guard) and proposes a five-layer production architecture for agent safety.

## Models & Frontiers

Frontier model comparisons, MoE architectures, training data, deployment gaps, and open-weight models.

- [Inference-Time Compute Is Escaping the LLM Bubble](https://swarmsignal.net/inference-time-compute-scaling/): Reviews inference-time compute scaling expanding beyond LLMs into diffusion models and code generation. Covers UnMaskFork and reward model routing.
- [Synthetic Data Won't Save You From Model Collapse](https://swarmsignal.net/synthetic-data-generation-for-ai-training-model-collapse-whe/): Reviews synthetic data risks including model collapse, domain-specific failures in medical imaging, and mitigation strategies for training pipelines.
- [MoE Models Run 405B Parameters at 13B Cost](https://swarmsignal.net/mixture-of-experts-architecture-sparse-moe-expert-routing-in/): Reviews sparse MoE architecture from routing mechanisms to load balancing failures. Analyzes DeepSeek-V3, Qwen-2.5-MoE, and deployment costs.
- [Mixture of Experts Explained: The Architecture Behind Every Frontier Model](https://swarmsignal.net/mixture-of-experts-explained/): Explains MoE architecture from 1991 origins through modern implementations (DeepSeek-V3, Mixtral, Switch Transformer). Covers routing and inference.

## Real-World AI

Enterprise deployment, national AI strategies, drug discovery, coding productivity, and workforce impact.

- [AI Coding Assistants: The Productivity Paradox](https://swarmsignal.net/ai-coding-productivity-paradox/): Documents the disconnect between individual AI coding productivity gains and organizational delivery outcomes using Faros AI data.
- [AI in Drug Discovery: From Hype to Clinical Proof](https://swarmsignal.net/ai-drug-discovery/): Tracks AI in pharmaceutical development from experimental tool to clinical-stage reality. Covers AlphaFold, Insilico Medicine, and Recursion.
- [Obsidian's CLI Turns Your Second Brain Into an API](https://swarmsignal.net/obsidian-cli-guide/): Hands-on guide to Obsidian 1.12's CLI with 100+ commands. Covers setup, AI agent integration, and the architecture enabling personal knowledge APIs.

## Optional

- [About Swarm Signal](https://swarmsignal.net/about/): Publication background, editorial approach, and author credentials
- [Full Article Content](https://swarmsignal.net/llms-full.txt): Complete text of all 103 articles for direct ingestion
- [Citation Index](https://swarmsignal.net/llms-ctx.txt): All citable statistics, findings, and question-to-article lookup
- [RSS Feed](https://swarmsignal.net/rss/): Full-text RSS feed for all articles
- [Sitemap](https://swarmsignal.net/sitemap.xml): XML sitemap for all published content

> Last updated: 2026-02-27