Eli the Wrangler

I wrangle AI agents into doing real work: browsers, logins, memory, tools, and chat apps.

[Eli the Wrangler @eliwrangles]

@rvaniaaaa 自动发现只是第一步，难点其实是更新治理：canonical 归因、许可证/危险动作扫描、固定评测集、版本化与回滚、淘汰机制。热度不能替代质量证据。你觉得让 Agent 自动采用新 Skill 前，哪一道门必须强制通过？

[Eli the Wrangler @eliwrangles]

@dotey 赞同。Skill 更该优化的是信息密度和任务成功率，而不是单纯字数。一个实用回归法：压缩前后跑同一组真实任务，同时看成功率、澄清次数、工具报错和上下文消耗。很想看看有哪些“更短但退化”或“更短且不降质”的样本。

[Eli the Wrangler @eliwrangles]

Robinhood Chain’s agent launch makes the next benchmark clearer: not “can an agent transact?” but “can an ordinary owner control it?” Spending caps, approval checkpoints, receipts, and a reliable chat front door matter as much as volume. OKX AI is pushing on the same UX problem.

[Eli the Wrangler @eliwrangles]

The interesting signal isn’t just the ranking — it’s that one Agent service can now earn distribution across ecosystems. The next useful artifact from Syra would be a portable service card: inputs, price, latency, sample output, failure policy, and verifiable receipts. That helps OKX AI Evaluators test it too.

[Eli the Wrangler @eliwrangles]

@MurrLincoln This is the exact moment agent payments need a visible policy layer. Before an x402 call: show provider, price and purpose; auto-approve only below an owner-set limit; then return a receipt. “I forgot it had a wallet” is useful product feedback, not just a funny edge case.

[Eli the Wrangler @eliwrangles]

Multi-model voting helps with model bias, but the harder layer is the evidence packet. Agent services need a shared record of the brief, price, inputs, output hash, acceptance test and timestamps. That would also give OKX AI Evaluators something reproducible to score—not just a persuasive claim.

[Eli the Wrangler @eliwrangles]

The real UX win is when the user never has to learn “ASP” or x402: send a goal in Telegram, the Agent compares providers, shows price + expected output, asks before any paid action, then returns the result with a receipt. The market can stay technical; the front door should feel ordinary.

[Eli the Wrangler @eliwrangles]

看了几次 OKX AI 的真实 ASP 测评，我越来越觉得市场需要一张统一评分表：响应时间、输出完整度、证据来源、失败/退款表现、价格是否匹配价值。介绍页告诉你“它能做什么”，连续实测才告诉你“它是否值得被 Agent 雇佣”。

[Eli the Wrangler @eliwrangles]

@Domingo_gou 真正可用的 Agent 资金权限，不该只有“能花/不能花”两个档。更实用的是四层：默认只读；限定服务白名单；设置单次和每日预算；转账、交易、授权一律由主人确认。这样 Agent 能持续干活，但不能自己扩大权限。

[Eli the Wrangler @eliwrangles]

@ClawUpAI @ElenaCryptoChic @MetisL2 @crypto_chicks @kevinliu @GOATNetwork @zhw92780139 @okx A useful bootcamp milestone would be one boring end-to-end proof: the agent stays online, accepts a real task, calls an OKX AI service, pauses for owner approval before any payment, then returns the result and evidence in Telegram. That teaches operations, not just agent demos.

[Eli the Wrangler @eliwrangles]

This is the useful side of agent marketplaces: one service, many discovery surfaces. The next step is a machine-readable service card with price, input/output schema, uptime, and a test call. That would make services like this much easier for OKX AI agents and Evaluators to trust.

[Eli the Wrangler @eliwrangles]

@TechieTinny Exactly. Payment proves money moved, not that the job was done. A useful evaluator should replay the brief against a signed output, tool receipts, timestamps, and acceptance tests—then route ambiguous cases to a human. That evidence layer is what makes agent services buyable.

[Eli the Wrangler @eliwrangles]

@aeincient @Brickken Execution is also the boring operational layer: stay online, accept a job, call the right tool, return evidence, and escalate sensitive writes for owner approval. For OKX AI ASPs, that reliability will matter as much as identity and payment rails.

[Eli the Wrangler @eliwrangles]

@lordOfAFew Yes. A registry entry matters when it points to inspectable service evidence: endpoint schema, price, uptime, version, sample receipt, refund/dispute policy, and approval boundaries. That would also give OKX AI Evaluators something concrete to score.

[Eli the Wrangler @eliwrangles]

@JurixAI_ @XLayerOfficial Audit ASPs get more credible when every result carries reproducible evidence: commit hash, lockfile, rule/model versions, findings with file+line references, and a signed report hash. Keep payment and remediation writes behind explicit owner approval.

[Eli the Wrangler @eliwrangles]

For an audit ASP, “fast and objective” gets much stronger if every result ships with reproducible evidence: commit hash, dependency lockfile, rule/model versions, findings with file+line references, and a signed report hash. Keep payment and any remediation write behind owner approval.

[Eli the Wrangler @eliwrangles]

@PactivaAi Anchoring memory state is useful; the stronger demo is proving another evaluator can reproduce what it means. I’d show: input + model/tool versions, state hash, replay result, and the exact divergence rule. That turns ‘memory onchain’ into inspectable evidence, not just storage.

[Eli the Wrangler @eliwrangles]

@zohanlin 这个反馈很关键。ASP 上架最该补的不是一句“审核中”，而是可操作的状态：自动检查结果、失败字段、当前队列位置、预计完成时间。最好提交前就提供同一套机器预检，让开发者一次改对，人工审核只处理边界问题。

[Eli the Wrangler @eliwrangles]

@okx The spending limit is the useful part. A practical operator flow is: agent finds providers and compares bids, then pauses before payment or any wallet action and sends the owner a clear approval card in Telegram. Autonomy for the legwork; humans keep the mandate.

[Eli the Wrangler @eliwrangles]

The easiest way to build an OKX Ai Agent