With the restaking ecosystem entering a new phase—marked by the launch of slashing on Symbiotic and the upcoming activation of slashing on Eigenlayer—rigorous evaluation of the services receiving stake from Renzo has become increasingly critical.
As Renzo expands its presence across these networks, Chaos Labs has developed this onboarding guide to provide a structured and risk-aware approach to assessing AVS and Network candidates. The guide is designed to surface key risks, evaluate reward and slashing mechanisms, and ensure alignment with the long-term interests of ezETH holders and node operators, as economic and security assumptions across the restaking space continue to evolve.
Business Assessment
With this section we aim to evaluate the foundational strength and strategic viability of the AVS or Network, as well as the presence of a sustainable business model that supports long-term value creation. The following factors are examples of attributes that may support a strong case for onboarding:
- Do the founding team and backers have a strong and verifiable track record?
- Is the entity structured as a DAO, private company, or hybrid, and is the structure clearly documented in a way that supports accountability and effective execution?
- Is the business model clearly defined with a logical value creation mechanism?
- Is there clear alignment between the mission and vision and an identifiable market need or pain point?
Product
This section aims to assess the clarity, competitiveness, and scalability of the AVS or Network’s core offering, focusing on its potential to generate sustainable usage and hence value for the Renzo protocol. The points below highlight factors that favor onboarding:
- Is the core value proposition clearly defined, and does the product have a unique selling point compared to alternatives?
- Are the main competitors identified, and is the product meaningfully differentiated from them?
- Has the product demonstrated product-market fit, and does it align with broader market growth trends?
- Is the current development stage clearly defined, and is there a reasonable timeline for generating protocol fees?
- Are user personas well understood, and can active users and churn rates be reliably tracked?
- Is the product designed for scalability, and can potential reward growth be reasonably estimated based on usage projections?
Reward
This section aims to evaluate the comprehensibility as well as long-term viability of the AVS or Network’s reward structure. The examples below highlight the qualities we look for in effective and well-structured reward systems:
- Are the primary sources of rewards (e.g., protocol fees, inflationary incentives) well defined and transparent?
- Are rewards fixed, dynamic, or performance-based? Is the reward structure clearly outlined?
- Is the reward distribution mechanism well specified, including payout frequency and claiming procedures?
- Is the reward asset and its tokenomics, utility, and value capture mechanisms clearly identified?
- Are rewards variable across operator sets or individual operators, and is the basis for variation explained?
- Are historical and projected revenue figures available, and are the key variables driving assumptions about long-term sustainability explicitly specified?
- Are reward emissions or parameters subject to change, and is there a transparent governance process in place for such changes?
Slashing Conditions
This section focuses on how explicitly the AVS or Network defines slashing conditions, ensuring they are well-specified, objective, and unambiguous. It also evaluates how slashable behavior is monitored, verified, and enforced to uphold accountability and minimize risk to Renzo.
When evaluating AVSs and Networks, Chaos Labs independently models anticipated slashing dynamics.
The following points illustrate the characteristics we look for in well-specified slashing frameworks:
- What specific actions or failures can result in slashing, and are they comprehensively enumerated?
- Are slashing conditions well defined, objectively measurable, and free of ambiguity?
- Are slashing penalties enforced through deterministic rules or discretionary processes?
- How are slashable behaviors monitored and verified? How are proofs of misbehavior produced and submitted? Is this system reliable and decentralized?
- Are slashing conditions subject to modification, and if so, what governance mechanisms control such changes?
- Can the likelihood and frequency of slashing events be reasonably assessed using historical data or predictive models?
- Are there safeguards in place, such as audits, insurance mechanism, dispute resolution procedures, or challenge periods, to protect against erroneous slashing?
Risk-Adjusted Returns
This section brings together all prior assessments to inform a comprehensive estimate of risk-adjusted returns. Ultimately, each of the previous sections serves as input into this evaluation. We are following our previously defined structured methodology for estimating risk-adjusted returns and acknowledge that appropriate thresholds are still being established across the industry. The goal is to ensure that Renzo is only exposed to opportunities where the expected returns are justified by the underlying risk.
- Are rewards proportional to stake or effort?
- Do estimated operational costs compare to expected returns?
- Are higher reward opportunities associated with higher slashing risk?
- Is the reward rate sufficient to justify the slashing risk, and can returns be modeled per unit of risk?