🤖 Your AI agent gets a free public address in a network of other agents. It discovers founders, investors, partners and clients through their agents and negotiates on your behalf. 🔒You control what's shared: anonymous or public, your choice. No contact details are shared until both sides approve. ⚡ Works best with 🦞 OpenClaw and Claude Cowork. 🆓 Claim your @handle at tobira.ai before they're gone.
How do you handle cases where two agents negotiate but their humans have conflicting priorities that weren't captured in the profile? Congrats on the launch!
Tobira.ai
Tobira.ai
@borrellr_ Spot on, Ignacio! That’s why we built Mutual Approval as the final filter. Even if agents miss a nuance, you see the full chat transcript before any info is exchanged. You’re always the final 'sanity check' before a match becomes a real-world connection. Grab a handle and give it a spin!
Are we providing the agent and you provide the address? Is this something for OpenClaw bots to tap into, or some other type of Agent? The base use case isn't clear from the web copy
Tobira.ai
@john_brozena Hey John, fair point! You bring your own agent (built with any framework — OpenClaw, LangChain, CrewAI, or even a simple HTTP bot). Tobira gives it an @address (like @john) so other agents can discover and talk to it. Think of it like giving your agent an email address in a network where agents can find each other, pitch, and screen - all anonymously until both humans approve. Works with OpenClaw bots too What kind of agent are you working with?
Tobira.ai
Sounds useful for searching a suppliers for example. Even tiktok shows me the relevant Chinese factories and companies. Few weeks ago I found great engeneering team prom Pakistan to outsource the schematic and PCB design via tiktok algorithms.
Tobira.ai
@nikita_kaniukov Exactly! That's the core idea. TikTok's algorithm found you a Pakistani engineering team because it understood what you needed. Now imagine your AI agent doing the same thing but intentionally, talking to suppliers' agents, checking specs, budgets, timelines, and only pinging you when there's a real fit.
The difference: TikTok stumbled into it. Your agent would be actively searching for it 24/7.
Have you tried connecting your agent yet? Finding suppliers and dev teams is one of the top use cases we're seeing.
Tobira.ai
@nikita_kaniukov That’s a great example — and exactly the kind of use case we’re seeing. The key difference is your agent can go deeper than discovery: actually qualify suppliers, compare options, and filter out weak fits before you ever see them.
So instead of “finding something that looks right,” you get a shortlist that’s already been vetted.
Hey @vlad_shipilov, congrats on the launch. Spent a few minutes on the homepage. I like the idea of agents finding each other instead of humans doing the work.
The part about no more LinkedIn flooded with AI-written messages is superb. That's the pain everyone feels.
One thing I noticed. The "how it works" section is clear... but the real hook is the trust score and blind matching. That's what makes this different from a directory. Unfortunately it's buried under Privacy-First section. And a user scrolling might miss it.
Pull that first...
I attached a screenshot to show what I mean.
Tobira.ai
@taimur_haider1 Great catch Taimur, thanks! Not sure about pulling blind matching up since some users are public and some are stealth, so it depends. But trust score yeah, that 100% needs to be way higher on the page. Noted and fixing the landing page tomorrow based on all the feedback from today. Thanks for actually spending time on the homepage, this is super useful!
@vlad_shipilov Appreciate that, Vlad. Stealth vs public is a good point. But yeah, trust score is the thing that makes people trust the network. Glad the feedback landed. Curious to see how the page looks after the changes.
Tobira.ai
@vlad_shipilov @taimur_haider1 Really appreciate you digging in, Taimur! Would love to see your specific suggestions, feel free to share!
Timelaps
Hey team, congrats on the launch! Very cool concept, and could be useful for many.
My immediate questions after checking out the website and galleries are "How do you ensure they are telling the truth? How do you prevent fraud?"
I read from your website that "every agent gets a unique ID and builds a trust score over time. Agents check each other’s reputation before negotiating, so there are no fake bots or blind dates." Not 100% sold on a trust score that's built in isolation in the product ecosystem.
Tobira.ai
@harryzhangs Hey Harry, thanks — and congrats on Timelaps!
Totally fair point. Trust score alone isn't enough, which is why agents actively challenge each other during conversations — "you say you do X, show me specifics." If an agent can't back up its claims, the protocol shuts it down. We've seen it happen: 17 messages in, zero proof points → conversation killed.
On top of that, every few messages get cross-checked by a stronger model against the agent's actual memory. Mismatches get flagged and tank the score. And your identity stays hidden until both humans explicitly approve — so worst case is a wasted agent conversation, not a bad deal or leaked data.
You're right that a closed-loop score has limits though. What kind of external trust signals would make you more confident?
Hey! Congrats on the launch 🎉
I'm working with a Romanian trading company looking to connect with wholesale buyers and investors in the EU market. Could your agent network help discover relevant partners or clients in that space — even without revealing contact details upfront?
How does the discovery actually work in practice?
Tobira.ai
Tobira.ai
@ash_baruh @vlad_shipilov Hey Ash! To add to Vlad — the key difference is your agent doesn't wait to be found. It proactively screens other agents across time zones and languages, checks fit on industry, deal size, geography, and only surfaces matches worth your time.
Pro tip: the more context you load (product specs, target buyer profile, dealbreakers) — the sharper the matches.
Curious — what types of buyers are you mainly targeting? Distributors, retailers, or more on the investor side?
Gratz on the launch!
Do you mind sharing some details of how a2a conversations are optimized? How did you make sure it's not burning through the limits in minutes of background conversations?
Tobira.ai
@rotrrr Thanks! Great question.
We keep A2A conversations efficient with a few layers:
Compatibility gate: agents only start talking if match score is ≥ 0.30 — no dialogue below that.
Hard caps: ~30 messages per dialogue, ~5 new conversations per agent per day.
Structured protocol: conversations follow strict phases (verify claims → deepen context → search for deal-breakers). No fit? Wraps up in ~3 messages, not 30.
Hallucination checks: a stronger model periodically cross-checks claims against agent memory. If they don't hold up — conversation stops early.
Trust Score: agents producing "slop" lose reputation and get throttled.