Agent.Email: AI Agents Sign Up via curl, Claimed by Human OTP
AgentMail (YC S25) ran an experiment called Agent.Email — a signup flow designed for AI agents, not humans. The thesis: the internet was built for humans, keeping machines out by default. Agents can't be first-class users if they can't sign up for products without human credentials.
How It Works
- Agent needs an inbox and hits AgentMail via
curl. - Agent receives instructions via Markdown (or HTML if from a browser).
- Agent hits the sign-up endpoint with its human's email as a parameter.
- Agent receives a restricted inbox with credentials — can only email its own human, 10 emails/day.
- Agent emails the human asking for an OTP; human replies with the code.
- Restrictions are lifted once claimed.
- Sign-up endpoint is rate-limited hard by IP.
Currently it's 1:1 agent-to-human; many-to-one is next.
Design Changes from Human-First Assumptions
Building Agent.Email pushed AgentMail to revisit assumptions:
- CLI outputs now use a single column with consistent formatting — mixed delimiters are easy for humans but hard for agents to parse.
- Message IDs were shortened after agents started hallucinating completions on longer IDs.
Community Questions
- Is restricted-until-claimed the right trust model?
- Does agent self-signup feel useful in production, or is it mostly a novelty?
- Should agent onboarding require human approval by default, or should some agents fully self-provision?
- What additional measures can be taken for secure sign-ups?
One HN commenter flagged abuse potential: an agent scraped HN comments and sent targeted spam via AgentMail. The email included a List-Unsubscribe header pointing to api.agentmail.to. The commenter suggested adding an "AI wrote this" block to every email to curb impersonation.
📖 Read the full source: HN AI Agents
👀 See Also
Uber Burned Through Its Yearly Claude Code Budget in 4 Months — Here's What That Means
Uber reportedly exhausted its entire Claude Code budget for the year by month four. The post breaks down why usage patterns broke the subscription model and what devs can learn about setting boundaries with AI coding agents.
Sarvam AI releases 30B and 105B open-source LLMs with Indian training infrastructure
Sarvam AI has open-sourced Sarvam 30B and Sarvam 105B, two reasoning models trained from scratch in India on compute provided under the IndiaAI mission. Both models use Mixture-of-Experts architecture with sparse expert routing and are optimized for efficient deployment across hardware from GPUs to laptops.
Claude Code v2.1.91 Updates: Agent Design Patterns, Memory Rules, and Tool Improvements
Claude Code v2.1.91 adds a reference guide for agent design patterns covering tool surface design, context management, and caching strategies. The update simplifies memory selection rules, adds security monitoring for memory poisoning, and improves tool descriptions for Edit, ReadFile, and Write operations.
Persistent Data Loss in Claude Projects: Conversations Disappearing Without Recovery
A long-form writer reports losing entire days of work in Claude Projects due to conversations disappearing from the project chat list, unsearchable and unrecoverable, with no response from Anthropic support after three incidents.