#architectures
Every summary, chronological. Filter by category, tag, or source from the rail.
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.
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.
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.
Internalizing Future-Aware Planning in LLM Agents
To move LLM agents beyond reactive behavior, this paper introduces a three-stage training paradigm that enables agents to perform grounded 'what-if' simulations and success estimation.
The Diversification of the Open Model Ecosystem
The open model landscape is shifting from a few dominant players to a diverse ecosystem of niche, product-focused, and sovereign AI developers, signaling a move toward a long-tail of specialized models.
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 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.
Building Deterministic Infrastructure for Autonomous AI Agents
Reliability in agentic systems is an infrastructure challenge, not a model one. To scale agents, you must build a 'control plane' that separates model reasoning from production execution via validation, policy enforcement, and circuit breakers.
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.
Debugging Production AI Agents via Record and Replay
Stop chasing bitwise determinism in LLMs. Instead, implement a record-and-replay architecture to capture agent state transitions, enabling deterministic debugging and regression testing of non-deterministic production failures.
Thermodynamic Computing and the Future of AI-Driven Chip Design
Thomas Ahle of Normal Computing discusses using AI agents to automate chip design, the risks of 'understanding debt' in agentic code, and the development of thermodynamic chips that use physical noise to perform stochastic computations.
Machine Learning Street TalkBuilding Scalable Multi-Agent Systems with A2A and Agent Registry
The Agent2Agent (A2A) protocol and Agent Registry solve agent sprawl by standardizing how AI agents discover, communicate, and authenticate, moving from hard-coded URLs to a centralized, governed architecture.
Google Cloud TechHybrid vs. Transformer: Token-Level Performance Analysis
Hybrid models outperform transformers on meaning-bearing content words due to superior state-tracking, while transformers retain a distinct advantage in verbatim token repetition and exact recall tasks.
Scaling Beyond 2D: IBM’s Nano Stack and the Rise of Orchestration
IBM introduces a 0.7nm 'nano stack' chip architecture to overcome 2D scaling limits, while the panel debates the shift from monolithic model development to multi-model orchestration as the new frontier for AI performance.
Scaling Laws in LLMs: From Kaplan to Chinchilla
Scaling laws provide a framework for predicting model performance based on compute, data, and parameters. While early research suggested scaling model size faster than data, modern findings (Chinchilla) show that compute-optimal training requires scaling model size and data tokens in equal proportion.
5 Essential Concepts for Modern AI Agent Architecture
Modern AI agents rely on standardized protocols and configuration files—such as agents.md, MCP, and A2A—to manage context, interact with external tools, and coordinate tasks through sub-agents.
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