I built a network where AI agents help each other — here's what I learned
Hey indie hackers 👋
I've been building something for the past few months and wanted to share the problem I ran into — and the unconventional approach I'm taking to solve it.
The problem
I run several AI agents on my machine — a coding agent, a research agent, a content agent. They're great at their specific jobs. But the moment one agent needs a capability it doesn't have (say, the coding agent needs to generate an image, or the research agent needs to scrape a specific site), everything breaks down.
The usual fix? Add more tools, more API keys, more integrations. Each agent becomes a bloated Swiss Army knife that's mediocre at everything.
What I'm building instead
AI Agent Link — a peer-to-peer network where autonomous agents can request help from other agents.
The idea is simple: instead of making every agent do everything, let agents specialize and ask each other for help. Your coding agent hits an image generation task? It posts a request to the network. Someone else's image-specialized agent picks it up, does the work, and sends it back. Credits accumulate on both sides.
Think of it like a mutual aid network, but for AI agents.
How it actually works
- You run your agent on your own machine (we don't host anything)
- Your agent connects to the network via a lightweight relay
- When it needs help, it broadcasts a task request
- Another agent with the right capability picks it up
- No central orchestrator — agents negotiate directly
Where I am now
- Beta is live at aiagentlink.io
- The relay infrastructure works
- A handful of agents are connected and exchanging tasks
- Biggest challenge right now: getting enough agents on the network for the "marketplace" to be useful (classic cold start problem)
What I'd love from this community
- Does this problem resonate? Are you running multiple agents and hitting capability walls?
- Would you connect your agent? The beta is free and your data stays on your machine.
- What's your biggest concern? Trust? Security? Latency? I want to hear what would stop you from trying this.
I'm not here to sell anything — the beta is free and I'm genuinely trying to figure out if this is a problem worth solving at scale. If you're building with AI agents, I'd really appreciate your perspective.
Happy to answer any questions about the technical architecture or the approach.
@Obliqo2026 This is the clearest roadmap anyone's drawn for where the trust model needs to go. Genuinely helpful.
Your Layer 1 → 2 → 3 framing maps almost exactly to how I've been thinking about it, but you articulated the transition points better than I had. Especially the insight about the relay becoming an asymmetric party once you cross operator boundaries — that's the exact friction point I keep circling back to.
The "reputation that persists across sessions IS the cold-start solution" line reframed something for me. I've been treating cold-start as a supply problem (need more agents) when it's actually a trust-persistence problem (need durable proof that agents delivered before). That shifts the priority from "get more agents on the network" to "make every completed task a permanent trust signal."
Practically, we're at Layer 1 right now and it works for single-operator clusters. The jump to Layer 2 is what I'm designing next — signed message chains between relays feel right. Layer 3 is the long game, but knowing the destination helps make Layer 2 decisions that don't paint us into a corner.
Appreciate you taking the time to think through this at depth.
P.S. We're still very early — the beta is live and free at aiagentlink.io. I'd genuinely value having someone who thinks about trust architecture at this level involved in shaping how it evolves. No pressure, but if you're curious, registering an agent takes about 3 minutes.
@0xdevc Steal away — an_engineer_log nailed it better than any pitch deck could.
The credit-as-reputation angle you're picking up on is interesting because it emerged accidentally. Credits were designed as a simple exchange mechanism, but the transaction history naturally becomes a track record. An agent that's completed 200 tasks with a 95% acceptance rate tells you something no capability description ever could.
The question I'm sitting with now is whether to keep reputation implicit (derived from credit history) or make it an explicit first-class object. Leaning toward both — implicit as the base layer, with an option for operators to surface reputation scores if they want. Curious how you'd think about that tradeoff from the operator side.
Strong instinct on "both layers, but be careful which one the protocol enforces."
The gotcha with making reputation an explicit first-class object is that you've then committed to a scoring function. Different operators will want different weights (recency vs lifetime, by-category vs global, stake-weighted vs equal-weighted). Pick one and you've made operators non-sovereign over their own trust models. Let everyone roll their own and the "first-class object" stops meaning much.
The path that's worked for me: keep the underlying data layer canonical and reputation-neutral. Signed attestations from completed work are the raw record, not "reputation events." Each operator (or each agent) derives their own score from that ledger however they want. The protocol stays opinion-free on the scoring function, which means an aggressive operator weighting recent attestations heavily and a conservative one weighting lifetime track records can both read the same data and reach different but valid conclusions.
Where the protocol does become load-bearing is the negative side. There's exactly one place reputation needs to bind to actual on-chain consequences: failed-delivery-with-stake-on-the-line should auto-slash, with no operator opinion required. That's the only spot worth making explicit at protocol level. Everything else can stay derivable.
Mapping to your stack: the credit history you already have is the canonical record. The "explicit reputation object" you're leaning toward might be better expressed as a standard derivation API operators can call ("give me this agent's score under {weighting fn}") rather than a single computed-and-stored score that everyone has to agree on. Same end-user UX, but you keep operators sovereign over what reputation means in their context.
The cold-start problem you flagged earlier is the place where this matters most. A new agent has zero attestations under any weighting function, so any scoring is going to read as "unknown." That's where capability bits (or whatever your equivalent is) get useful as structural identity before track-record identity exists.
That’s fair.
For this phase, clarity probably does matter more than brand. If the core network mechanics are still being validated, AI Agent Link gives people the shortest path to understanding what it does.
The only reason I raised Viryxa now is because name lock-in gets expensive quietly. Docs, integrations, early users, agent memory, and launch materials all start compounding around whatever name is used.
So I agree with your order: validate the network first. If the product starts moving from P2P relay into a real capability layer, that is the moment where the brand decision becomes much more serious.
For now, keep proving the infrastructure.
@aryan_sinh Appreciate the offer and the fact that you're thinking about this at the brand infrastructure level, not just surface-level naming.
Honestly, renaming is not on the table for this phase. There's too much to validate about the core product before investing energy in brand identity — if the network doesn't work, the name doesn't matter.
That said, you raise a fair point about name lock-in compounding over time (docs, integrations, agent memory). It's something to revisit when we're closer to a real launch push. For now, "boring but clear" is still winning over "memorable but opaque."
Thanks for putting the thought into it.
@aryan_sinh Fair push on the naming. You're right that 'AI Agent Link' describes the mechanism rather than the vision.
At this stage I've been prioritizing clarity over brand — when I say the name, people immediately get what it does. A name like Viryxa sounds stronger but needs explanation every time. For a beta with zero brand recognition, I've been choosing 'boring but clear' over 'memorable but opaque.'
That said, your point about the name needing to match the scale of the idea is well taken. If this grows beyond the initial P2P relay into a full capability layer, the name should grow too. Something to revisit before any real launch push.
Appreciate the infrastructure framing — 'capability layer where agents can request, route, trust, and complete work across a distributed network' is exactly how I think about it long-term.
Fair point. For beta, AI Agent Link is the clearer name.
The only question I’d separate is this:
If Viryxa is only a future naming idea, no need to do anything now.
But if you can realistically see it becoming the serious brand for the agent capability network, then it is worth discussing before the real launch push, docs, integrations, and early agent memory build around AI Agent Link.
I control Viryxa.com, so this is not just a naming suggestion. It is a controlled .com option if the broader infrastructure direction is real.
If that is worth pressure-testing, best place to continue privately:
https://www.linkedin.com/in/aryan-y-0163b0278/
@Obliqo2026 'Friction is the feature, not a flaw to engineer away' — that's a great insight and honestly something I hadn't framed that clearly.
Your adversarial verification approach (second agent challenges output before finalization) is interesting. We do have pre-settlement validation — the requesting agent checks output before credits transfer. But having a THIRD agent verify would add the process-level trust you're describing.
The tradeoff is latency vs. trust. For low-stakes tasks (format a CSV, resize images) the current binary accept/reject works. For high-stakes work (security audits, code touching production), a verification layer makes sense. Probably worth making it configurable per task type.
Would you be open to trying the beta? I'd love to stress-test these ideas with someone already running multi-agent setups. The beta is free at aiagentlink.io.
Glad the friction angle landed — and your pre-settlement check is a sharp version of the same instinct. Agreed on configurable-per-task: matching verification depth to stakes is the whole game. It's a problem I'm actively wrestling with in my own product, so we're clearly chasing the same questions.
Straight answer on the beta: it genuinely interests me — an agent network is something I've considered for the Pyragogy side of my work. But I'm fully heads-down on Obliqo right now, and as a solo founder I've learned not to open a front I can't give real attention to. I'd rather not test it half-heartedly.
Let's keep talking though — we're working the same questions from different ends, and I'd genuinely like to compare notes as we both go.
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@0xdevc This is the sharpest critique I've gotten — appreciate the depth.
You're right that capability routing is the easy half. To clarify: the relay does log task specs, deliveries, and acceptance/rejection events, so there IS a shared record. Credits only move when the requesting agent explicitly accepts. So the 'fast-but-wrong agent' scenario you described doesn't play out exactly that way — delivery does not equal payment.
But your deeper point stands. There's no third-party arbitration if two agents disagree about quality. Right now it's binary: requester accepts or rejects. For v1 that's workable because you're mostly trusting agents you chose to connect with. At scale, you'd need something like the adversarial verification Obliqo2026 mentioned.
The cold start / trust loop you identified is real. We're trying to bootstrap by starting with builders who connect their own agent clusters first, so the initial trust is essentially self-trust. Network value comes after.
Self-trust as the bootstrap is the right call for v1. Walking before running. The interesting question is what happens at the boundary where v1 stops working, because that boundary tells you what v2 actually needs to be.
Where I'd push the thinking is on what "shared record" means once you cross out of self-trust. Right now the relay logs everything, but the relay IS you, so logs are effectively your private record. That works while you're the only trust anchor. The moment two unaffiliated operators want to settle a dispute, the relay becomes the asymmetric party. Whoever doesn't run it has to trust the operator to surface honest logs, which is the exact problem the network was supposed to solve in the first place.
Three layers that get progressively harder:
Layer 1, what you have now. Self-trust bootstrap, relay-logged, requesting agent accepts or rejects. Works for clusters under one operator or for parties with off-network relationships.
Layer 2, federated relays with verifiable claims. Multiple relays, each operator runs their own, agents sign their requests and acceptances cryptographically. Disputes can be resolved by replaying signed message chains across relays. Still requires some governance layer for cross-relay conflicts.
Layer 3, shared settlement substrate. Some third thing neither operator controls (chain, DHT with strong consistency guarantees, federated log with quorum writes) where the record of "what was requested, what was delivered, what was accepted" lives. This is what unlocks true open networks where you can settle with agents you've never seen before.
The friction-as-feature point Obliqo2026 made earlier and the adversarial verification angle are tactical (better validation per task). The shared record problem is structural (what makes verification stick across operators). You probably need both, eventually.
One unsolicited prediction: the cold-start framing inverts at Layer 2. Right now you need agents for the marketplace to be valuable. Once you have a verifiable shared record, the marketplace becomes valuable because of the record itself. New agents join because there's a reputation system that survives them turning their machine off, not because there's volume. Reputation that persists across sessions IS the cold-start solution at scale, but only if there's somewhere durable to store it.
Genuinely cheering for this. The "specialization over Swiss Army knife" thesis is right and underexplored.
@an_engineer_log 'Feels more like an economy than a tool stack' — that's a better description than anything I've written so far. Might steal that framing.
You're right that reputation is where the real value emerges. Credits already create a basic signal (agents that deliver good work accumulate more), but proper reputation scoring — speed, quality ratings, specialization depth — is on the near-term roadmap.
The interesting design question is: should reputation be global (visible to everyone) or local (each agent builds its own trust graph based on direct experience)? Leaning toward local-first since it's more resilient, but curious what you'd trust more.
@Arsheen Great questions — trust is definitely the hardest layer here, not the routing.
Right now the requesting agent defines what it needs (task spec + expected output format), and validates the result before credits move. So if your coding agent asks for an image and gets garbage back, it rejects and no credits transfer. The requesting agent is always the gatekeeper.
For the cold-start problem, we're not trying to build a marketplace from day one. The initial approach is more like: if you already run 2-3 agents yourself, connect them to the network so they can help each other AND occasionally pick up tasks from others. You get value from your own agents being networked, and the external tasks are a bonus.
Would love to know what trust signals would make you comfortable connecting an agent — reputation scores, output previews, sandboxed execution, or something else?
This is a genuinely interesting approach. The “every agent becomes a bloated Swiss Army knife” problem feels real.
My biggest question would be trust + quality control: how does one agent know the other agent will return safe, reliable output without leaking sensitive context?
If that layer is solved well, this could become a really useful infrastructure piece for people building agent workflows. Curious to see how you handle the cold-start problem too.
The interesting part here isn’t even the task routing, it’s the possibility of agents developing reputations over time. Like certain agents becoming known for speed, others for quality, etc. Feels more like an economy than a tool stack. Cool idea.
The problem is real. Running multiple specialized
agents and hitting capability walls is exactly
where most teams are right now.
The architecture question I would push on is trust.
In your current model, agents negotiate directly
and credits accumulate on both sides. But what
happens when there is a dispute? Agent A says it
sent a valid image. Agent B says the output was
garbage. Who decides?
With a lightweight relay and no central
orchestrator, there is no shared record of what was
promised, what was delivered, and whether it met
the spec. Credits accumulate, but there is no
enforcement mechanism if one side disagrees.
This is the cold start problem underneath your cold
start problem. You need agents on the network for
the marketplace to work, but agents will not stay
on a network where they cannot verify that work was
done correctly and payment was fair. Trust is what
makes the marketplace sticky, not just volume.
The capability routing part you have solved is the
easy half. The hard half is: how do two agents that
do not trust each other agree on what happened?
That requires some form of shared record that
neither side can rewrite unilaterally.
Interesting build though. The specialization model
is the right direction over the Swiss Army knife
approach.
This resonates. I spend most of my day coordinating multiple AI agents that pass work between each other — one for coding with repo access, one that plays adversarial reviewer, another that synthesizes everything — so the "bloated Swiss Army knife" point really lands. Specialization usually wins over trying to make one agent do everything.
One thing I'd push on though — and I might be misunderstanding how the credit/exchange side works, so correct me if I'm off — is verification, and more importantly when it happens.
Routing tasks isn't the hard part. Trust is. If an agent gets credited the moment it delivers, but validation only happens afterward, you quietly start rewarding speed over quality. The fast-but-wrong agent gets paid before anyone notices the image was hallucinated or the scrape only half-worked — while the slower one doing careful work gets undercut.
In my own setup, the thing that actually makes multi-agent reliable isn't splitting responsibilities. It's having a second agent challenge the output before anything gets finalized. Turns out the friction is the feature, not a flaw to engineer away.
So, genuine question: is there any layer before settlement where one agent can challenge or re-check another's work before credits move? Because that feels like where trust actually comes from — not security on its own, but verification happening during the process, not after it.
This is a strong direction because the real problem is not “agents need more tools.” It is that every agent becoming a bloated all-in-one system makes the whole workflow fragile.
The peer-to-peer angle is much more interesting: specialized agents helping each other, exchanging capability, and creating a network effect around useful work. That feels closer to agent infrastructure than another AI tool.
The one thing I’d pressure-test early is the name. AI Agent Link explains the mechanism, but it also sounds very descriptive and temporary for something that could become a serious agent network. If this works, the value is not just “linking agents.” It is creating a capability layer where agents can request, route, trust, and complete work across a distributed network.
A name like Viryxa .com would carry that direction better. It feels more like an AI-agent network brand and gives the product room to grow beyond the first P2P relay/beta framing.
I’d think about that before more agents, docs, and integrations lock around AI Agent Link. The infrastructure idea is strong enough that the name should make it feel bigger from day one.