Tag
This paper explores using large language models and AI agents for autonomous chip design, modeling it as an AI-organization and discussing action spaces for black-box optimization in chip design scenarios.
The article discusses how the architecture and connections between agents in multi-agent AI systems are more critical than the agents or models themselves, using Grok Bot to demonstrate how wiring diagrams determine performance.
The tweet argues that running too many AI coding agents in parallel degrades codebases and advocates a structured setup with a few specialized agents. It also quotes the launch of Jcode, an open-source agent claiming 20x memory efficiency.
An essay observing an architectural shift where LLM agents orchestrate deterministic code instead of deterministic code calling LLMs, with practical red and green flags for when this inversion makes sense.
Prime Intellect launches Prime Agent, a fully open-source self-improving coding harness built around Recursive Language Model (RLM) and Continual Harness abstractions, enabling persistent sub-agents and dynamic tooling via a REPL-based interface.
Shubham Saboo discusses how graphs make agent organizations programmable, contrasting static workflow graphs with dynamic agent orgs that rewrite themselves during execution, and cites FarmTable as an example.
LoopX is an open-source control plane for ultra-long-horizon AI agents. By externalizing structured state (todo, authority, evidence, gate, etc.), it enables agents to run continuously for 200+ hours without memory loss or drift, and uses an executable Kanban and a six-layer architecture to manage long-horizon tasks.
GraphArc is an open-source tool that visualizes AI agent workflows as interactive, real-time graphs, enabling users to inspect, debug, and approve agent actions before execution to make agentic AI more explainable and controllable.
A practitioner shares real-world challenges in deploying AI agents to production, highlighting that governance, auditing, and deployment guardrails are now the bottleneck, not agent building, and notes emerging solutions like Lyzr Control Plane and Microsoft's reference architectures.
A hot take from scotups argues that models are no longer the differentiator; instead, building the best harness (e.g., OpenCode, OpenClaw, Hermes, Pi) is key, as discussed in a course on Harness Engineering & Agent Orchestration.
AutoDev Studio is an open-source, model-agnostic multi-agent SDLC harness that orchestrates a chain of agents to automate the software development lifecycle, reducing costs by 7–75% compared to Claude Code for similar tasks.
A proposal to disambiguate the term 'graph engineering' into 'knowledge graph engineering' and 'agent graph engineering', referencing the 2026 surge in agent orchestration graphs and the confusion with traditional knowledge graphs.
Lightning Orchestrator is an agent skill that uses fast subagents (SWE-1.7 Lightning) for implementation, achieving ~5x faster execution while keeping a frontier model as planner and reviewer. It works with Devin and supports parallel execution for larger tasks.
Discusses the challenge of managing identity for agents dynamically spawned by an orchestrator at runtime, a key concern in multi-agent systems.
The author describes the hassle of manually coordinating multiple AI coding agents and introduces Accord Agents, an open-source shared workspace that enables agents to discuss and review each other's work while keeping the process transparent to the human.
Jamon Holmgren shares his comprehensive agentic development setup, including workflow docs, self-healing docs, cross-agent review, automated testing, and autonomous agent loops.
After 3 months running AI agents in production across 3 SaaS products, the author shares what worked (GitHub MCP, Postgres MCP, Playwright MCP) and what broke (long tasks, auth walls, cost blowups, multi-tool orchestration errors), with a monthly cost of ~$430.
This paper formalizes the execution harness around LLM agents as a learnable control layer using a Harness MDP trained via offline reinforcement learning, showing improvements in verification behavior and final quality across domains.
This article introduces Onyx, a VM for programmable agent orchestration, turning agent orchestration into software engineering by providing a runtime with clear semantics and execution model. It discusses the design decisions and limitations of current agent systems, and presents the PROGRAMS specification.
Alook is an open-source platform that turns coding agents into a real org chart, allowing users to build an AI-run company with minimal employees. The tutorial shows how to set up a four-agent team to handle competitive intelligence tasks.