Agents & Orchestration
All things Agents & Orchestration on Edge.
Agent-Native Immune System (ANIS): Architecture for Runtime Defense
The Agent-Native Immune System (ANIS) shifts AI security from static training-time alignment to dynamic, runtime defense, using a six-layer 'Immune Tower' to protect autonomous agents against memory poisoning and tool-chain manipulation.
ATOD: Hybrid Distillation for Autonomous Agent Training
ATOD combines on-policy distillation with reinforcement learning using an annealed schedule and turn-level reweighting to train small agent models that outperform their larger teacher models.
Odyssey: A Categorical Framework for Verifiable Foundation Models
Odyssey uses categorical sheaf theory to compose modular 'foundries'—verifiable, truth-preserving architectural components—that allow for structured, queryable, and auditable LLM-based systems.
ToE: Hierarchical Claim Verification Against Adversarial Misinformation
Tree of Evidence (ToE) is a fact-checking framework that uses a reinforcement learning-driven agent to decompose claims into hierarchical argument trees, significantly improving verification accuracy against adversarially poisoned inputs.
Improving Long-Horizon LLM Planning via Symbolic Feedback
This framework enhances LLM planning reliability by using a symbolic verifier to identify errors and provide corrective, interpretable instructions for iterative self-refinement.
AI-ModelNet: A Networked Architecture for Collaborative AI
AI-ModelNet proposes a hierarchical, Internet-inspired architecture to enable interconnection and collaborative reasoning among heterogeneous, domain-specific models, addressing the fragmentation of the current AI landscape.
Personality Prompting in Multi-Agent Teams: Task-Dependent Impact
Personality manipulation in LLM agents significantly alters communication style but only degrades task performance in open-ended or collaborative domains, while remaining largely neutral in structured coding tasks.
Building and Auditing Local Coding Agents
A practical guide to setting up a local coding agent stack using Ollama and open-weight models, emphasizing performance benchmarking, secure auditing of agent harnesses, and the trade-offs of running local vs. proprietary infrastructure.
Claude Tag: Moving AI from Chat to Team-Based Delegation
Claude Tag shifts LLM interaction from synchronous chat to asynchronous, team-wide delegation within Slack, positioning Claude as a persistent, proactive coworker rather than a standalone tool.
The Rise of Meta-Harnesses and Vertical AI Integration
The AI industry is shifting toward 'meta-harnesses'—standardized agent orchestration layers—while frontier labs move toward vertical integration of custom silicon and agent-native UX.
Internal AI Adoption & The Rise of Agentic Workflows
OpenAI reports massive internal token growth across all departments, signaling that agentic workflows—supported by review loops and persistent infrastructure—are moving from experimental to core production patterns.
Claude Tag: Collaborative Agentic Workflows in Slack
Claude Tag integrates Claude into Slack as a persistent, multiplayer agent capable of autonomous task execution, cross-channel context awareness, and proactive collaboration.
Agentic Robotics, Large-Scale Infra, and Future Uncertainty
Recent developments in agentic robot self-improvement, large-scale GPU cluster telemetry, and legal data infrastructure highlight the rapid maturation of AI systems, even as experts debate the long-term implications for human autonomy.
The Future of AI: Shifting from Monolithic Agents to Composition
Justin Schroeder argues that the future of AI lies in 'domain-specific agents'—small, specialized, composable units—rather than monolithic agents, to solve the reliability, cost, and complexity issues inherent in current agentic architectures.
The Agentic AI Engineer: Scaling Agent Development via Loops
To scale agent development, teams must move from manual iteration to an 'Agentic AI Engineer' model: a multi-agent system that automates the entire lifecycle of spec, build, eval, diagnose, and optimize.
The Prompt as a Platform: Agentic Engineering for Distributed Systems
Dominik Tornow argues that software engineering is shifting from general-purpose implementations to bespoke systems synthesized by agents from abstract specifications, using deterministic simulation as the critical feedback loop for design.
RL-Guided ETL Pipeline Remediation: Architecture and Evals
Automate ETL failure recovery using a deterministic anomaly detection layer, a Q-learning policy for action selection, and a hard-coded safety guardrail to ensure operational reliability.
Building Low-Latency Voice-In, Visuals-Out AI Agents
To achieve a seamless AI UX, shift from voice-in/voice-out to voice-in/visuals-out. This leverages the human brain's visual processing capacity and a more forgiving 1-second latency budget compared to the strict 200ms required for fluid speech.
AI EngineerBuilding Full-Stack Apps with AI Sub-Agents
Google Antigravity uses voice-prompted sub-agents to orchestrate complex full-stack development, leveraging specialized guidance and MCP tools to build, test, and deploy multilingual applications.
The Shift from Chatbots to Agentic Workflows
OpenAI's internal data shows a transition from short-horizon chatbot interactions to long-horizon agentic tasks, with non-technical departments adopting agents faster than engineers to perform cross-functional work.
Scheduled Work: Task vs. Message Architectures
Distinguish between scheduled tasks (fresh threads) and scheduled messages (persistent threads) by asking if the job requires the context of previous runs.
Automating Android Tasks with Gemini 3.5 Flash Computer Use
Gemini 3.5 Flash's native 'Computer Use' capability allows LLMs to control Android devices by interpreting screenshots and executing actions via ADB. This guide provides a framework to bridge model function calls to device inputs.
Building Production-Ready Agentic Apps with CUGA
CUGA (Configurable Generalist Agent) is an open-source harness that abstracts agent plumbing—planning, state management, and tool execution—allowing developers to build production-ready agents by defining only tools and prompts.
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