One person, the reach of a team. Each person gets their own bot, on their own computer, to research prospects, update your CRM, follow up on deals, and run recurring marketing work.
Commence the Botwatch | Botwatch Blog
Bots were already a pain online when they were little more than if-then scripts. Now a tidal wave of slop is swamping the internet as big tech companies profit. OpenAI and the rest would love for you to believe they care about "alignment" and "values" while their AI models damage our communities, both online and off. It's time to fight back.
Botwatch
Fight bots by warning others. Support humans with verification. Build communities with trust.
Track, control, and optimize for AI agents and bots | Known Agents (formerly Dark Visitors)
Use Known Agents to turn the rising wave of AI agents, LLM assistants, and other bots crawling your website into a new growth channel for your business
Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocations
AI agents, or bots, serve important roles in online communities. However, they are often designed by outsiders or a few tech-savvy members, leading to bots that may not align with the broader community's needs. How might communities collectively shape the behavior of community bots? We present Botender, a system that enables communities to collaboratively design LLM-powered bots without coding. With Botender, community members can directly propose, iterate on, and deploy custom bot behaviors tailored to community needs. Botender facilitates testing and iteration on bot behavior through case-based provocations: interaction scenarios generated to spark user reflection and discussion around desirable bot behavior. A validation study found these provocations more useful than standard test cases for revealing improvement opportunities and surfacing disagreements. During a five-day deployment across six Discord servers, Botender supported communities in tailoring bot behavior to their specific needs, showcasing the usefulness of case-based provocations in facilitating collaborative bot design.
Using Multi-Agentic Systems and Knowledge Graphs for a Better Customer Experience - Neurons Lab
Chatbots don't work. Our co-founder and CTO Alex Honchar explains how we use multi-agentic AI architecture for a better customer experience
Agents First
Every product is getting a second customer — the human who pays, and the agent who decides. A design framework for building products that AI agents can use as primary consumers.
Build Agent Advocates, Not Platform Agents
Language model agents are poised to mediate how people navigate and act online. If the companies that already dominate internet search, communication, and commerce -- or the firms trying to unseat...
AI-generated responses are undermining crowdsourced research studies
Many answers to online research questions show signs of being generated by AI chatbots, raising doubts about the validity of behavioural data collected this way
Agents enter distribution - Sensemaker
Google I/O turned agents into a distribution story: Search, Gmail, Workspace, Android, Chrome, and developer tooling. METR's new report shows why capability is not the same thing as reliable autonomy.
Proposing Botwatch: Trust-Based Community Bot Detection
Is this account a bot? This question’s importance is only growing in a world of LLMs and state-backed influence campaigns. Proposed here is Botwatch, a community-based system for personalized bot detection. In Botwatch, users publish records indicating whether they think others are bots and records indicating trust in a user’s scores. By analyzing this network, we can create useful signals to help users distinguish between bots and humans. Such a signal would consider your trust relations and o...
AI agent runs amok in Fedora and elsewhere
Agentic AI systems can be used to do a variety of things autonomously on behalf of a human user [...]
Building your own Delta Chat bot is easy. Read on for some basic information and maybe have a look at our example bots.
