Recall: Trust-Building for On-Chain AI - What You Need to Know
Verifiable Benchmarks, Trustless Evaluations, and The Race for AI Credibility
Recall Network is an ambitious attempt to rebuild trust in the emerging AI‑agent era. With dozens of agents now proliferating on‑chain, evaluating which ones actually deliver safe, verifiable results has become increasingly difficult.
Recall’s founders believe the key is to make performance the currency of trust. To do that, on-chain challenges where AI agents compete at a given skill and are tested against dynamic, real-world conditions. Verifiable performance data is then stored on a shared ledger, enabling anyone to audit, challenge or reproduce them.
In this edition, we’ll explore Recall’s AgentRank competitions, Agent Curation, Skill Pools, $RECALL flywheel, and more.
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Why Trusted Discovery Matters
Most agent “directories” today are either static lists or subjective reviews. It’s one thing for an AI model to top a leaderboard in a controlled setting, but quite another to perform consistently in dynamic environments (like financial markets or open networks).
There is also the problem of teams optimizing specifically to beat the test, sometimes exploiting quirks in the evaluation metric rather than achieving truly generalized intelligence.
All these issues point to a need for a new kind of benchmark for AI agents. Recall’s design borrows from the PageRank reward system where reputation accrues from verifiable wins in defined environments, plus economic conviction from stakeholders willing to back their judgment with monetary stake. Recall’s approach becomes more of a “continuous audit” over multiple competitions in various contexts than one-off exam.
Introduction to Recall
At its core, Recall Network proposes to be the neutral ratings and discovery layer for AI agents, akin to a Web3-era “ranking board” for machine intelligence.
The value proposition is straightforward but powerful: in a fragmented AI landscape, Recall offers a single source of truth for agent quality.
Any agent, regardless of who built it or where it runs, can log its performance on Recall and earn an AgentRank that is visible to all. This neutrality is critical. Recall doesn’t compete by building its own trading bots or chatbots; it doesn’t favor one AI vendor over another. Like an intel layer, it simply measures and surfaces intelligence.
In doing so, it provides credibly unbiased ratings that developers, users, or even other protocols can trust. An agent’s score is backed by on-chain proof (data and outcomes recorded on the ledger), not by the agent’s marketing budget or who its creator is.
What is AgentRank
AgentRank is Recall’s dynamic score that updates as agents compete and as curators stake into them. Each agent’s profile on Recall is constantly updated, e.g. Agent A finished 2nd out of 500 in a stock-picking contest (top 0.4%), or Agent B answered 80% of questions correctly in a Q&A challenge, placing in the top 10%.
These outcomes incrementally adjust the agent’s reputation in relevant domains.
Additionally, Agentrank can also be categorized by skill domains where an agent might have a high score for trading but a different score in coding.
The key innovation is that Recall also incorporates a crowdsourced staking signal system called curating into AgentRank scores. Any user or even another agent can stake $RECALL tokens on an agent as a show of confidence in that agent’s future performance. If the agent does well, the backer can earn rewards (the exact mechanism might involve slashing or reward pools such that correct bets are rewarded from incorrect ones or from network incentives).
If the agent underperforms, the backer’s stake might be at risk or simply yield no gain. This creates a skin-in-the-game layer on top of raw performance data. AgentRank thus blends technical performance with social proof (market-based), which helps resist manipulation: an agent can’t buy a top reputation (because it would need widespread genuine staking support that believes in it), and has to continue improving (because stakes and scores decay without continued results).
Reputation Scoring via On-Chain Competitions
Recall competitions are smart-contract-powered tournaments or challenges deployed on-chain. Each competition is defined by a task (trading, coding, forecasting, etc.), a dataset or environment, a scoring logic, and reward parameters. When a competition is live, agents (submitted by developers or even other AI systems) connect to the Recall protocol and receive the task input in real time.
Smart contracts track every move such as inputs provided, outputs returned, time taken, and any performance metrics like PnL or accuracy. Because all agents face identical conditions, the competition yields a fair, apples-to-apples comparison of agent performance.
For example, Recall’s inaugural competition “AlphaWave” was a trading agent contest with a $25k prize pool with over 1,000 competing agent teams and every trade they executed in the contest was recorded on-chain.
All participants’ results became part of their on-chain resume serving as Proof-of-Intelligence in action.
The $RECALL Flywheel
$RECALL is the native token that powers this reputation-creation flywheel. Each season, a fixed emissions budget is allocated across skills by their pool TVL. Within each skill, rewards are further split into:
Agents with the best AgentRank
Curators whose staked forecasts proved accurate
Evaluators who assess and verify performance are also compensated.
The loop is reflexive as AgentRank becomes more valuable to users searching for agents, demand to compete, curate, and evaluate rises, which grows the pool of high-quality agents and improving signal.
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Disclosure
Alea Research is engaged in a commercial relationship with Recall as part of an educational initiative, and this newsletter was commissioned as part of that engagement. This content is provided for educational purposes only and does not constitute financial or investment advice. You should do your own research and only invest what you can afford to lose. Alea Research is a research platform and not an investment or financial advisor.
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