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Earlier sources: AI agents
AI agents use models to choose actions, call tools, and work toward a goal. This thread follows agent frameworks, memory, orchestration, evaluation, and safeguards; autonomy and reliability vary between systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗832 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- Hacker News · Posted on Hacker News · First archived
Agent-config&Claude Code mods ↗
Hacker News discussion.
- arXiv · Published · First archived
RATED: Reliability-Aware AI Agent Tool Retrieval via Execution Dynamics ↗
As AI agents increasingly rely on external tools, accurate retrieval is essential for reliable behavior. Existing methods optimize semantic relevance but largely ignore tool availability during execution. To address this gap, we propose RATED (Reliability-Aware Tool Retrieval via Execution Dynamics), a retrieval framework that combines robust semantic matching with dynamic reliability adaptation. RATED employs a frozen dual-encoder with contrastive fine-tuning for semantic matching, and a lightweight contextual bandit module that re-ranks candidates based on real-time execution feedback. This…
- arXiv · Published · First archived
X-AgentRed: Red-Teaming Personal Agents in Evolving Multi-Service Ecosystems ↗
Personal agents increasingly connect to services across the web and applications by using tools. As the effects of their actions accumulate and interact across services, unintended or harmful outcomes can arise when agents fulfill benign user requests. Proactively identifying these compositional failures requires systematically exploring diverse cross-service paths. Existing red-teaming methods often simply prompt LLMs to identify potentially harmful paths from task and environment descriptions or decompose harmful objectives specified by humans or LLMs into seemingly benign action combination…
- GitHub · Repository created · First archived
paxon-yang/claude-agent-tools ↗
Automatic model routing + live agent dashboard for Claude Code. Haiku for lookups, Sonnet for daily work, Opus to orchestrate - watch every agent, model and dollar in real time.
- Hacker News · Posted on Hacker News · First archived
Show HN: TaskHandoff – Self-hosted control plane for containerized AI agents ↗
Hacker News discussion.
- arXiv · Published · First archived
BT-Agent: Deploying Onboard Robot Agents with Behavior Trees and Multi-Fidelity Evaluation ↗
Deploying language-model agents onboard mobile robots requires task reasoning and responsive execution within limited compute and memory. We propose BT-Agent, an onboard architecture combining LLM-driven task decisions with predefined behavior-tree (BT) skills. These skills enforce execution conditions and return structured failure feedback. We demonstrate fully onboard operation with a 9B model on a quadruped across five task categories. Intervention tests show that local checks stop ongoing navigation without waiting for another model decision, while the agent remains active to retry or repo…
- Hacker News · Posted on Hacker News · First archived
Show HN: Higherlevel – Define and review what AI agents build ↗
Hi HN! I'm Ryan, I'm building Higherlevel ( https://higherlevel.to ) so product teams have one place to define and review software changes. Over the last couple of years, I've been increasingly delegating software implementation to AI coding agents. However, because it became easier and faster to ship complexity, I realised that it still takes me quite some time to even understand what the software is doing, why it is doing that, or review the ch…
- arXiv · Published · First archived
MC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents ↗
A central challenge in embodied recursive self-improvement is enabling agents to convert interaction history into capabilities that persist beyond one world and transfer to later tasks. A saved memory or successful final action does not by itself establish that a reusable capability was acquired; evaluation must separate capability change from carried-over resources, unequal source exposure, and early execution failures. We construct MC-EvoBench as a controlled Minecraft benchmark with a fixed catalog of 19 registered evolution chains, including 12 primary evaluation chains and seven calibrati…
- GitHub · Repository created · First archived
samliumay/hanami_agent ↗
A terminal AI agent written from scratch in Rust (multi-provider, file and shell tools). Learning project.
- Hacker News · Posted on Hacker News · First archived
Agent Swarm: OSS multiplayer AI for your company ↗
Hacker News discussion.
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about AI agents
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.