How FereAI Uses EigenAI’s Determinism to Make Trading Agents Reliable and Verifiable

Ask an LLM the same question twice and you might get two different answers. For a chatbot, that's quirky. For an AI agent managing your trading strategy with real capital, that's unacceptable. This case study explores how EigenAI has solved the determinism problem for FereAI that's kept AI agents as "functional toys" rather than serious financial infrastructure.

What is FereAI?

FereAI is a crypto assistant built for long-horizon execution. Agents ingest onchain data, market news, and social signals, then produce research and decisions that are deterministic and auditable. The goal is simple: reproducible intelligence you can actually rely on when real money is at stake.Core surfaces include a Pro research agent, Market Pulse (real-time news and social chatter), trading and investment agents, and an Alpha Dashboard that refreshes every 60 seconds. For developers, FereAI exposes REST and WebSocket APIs, plus an agent framework (0xMONK) capable of launching thousands of concurrent agents.With over 7,000 daily users relying on FereAI for research, alerts, and agent workflows, the platform needed to solve a problem that has plagued AI systems since their inception: reproducibility.

The Challenge

Before EigenAI, FereAI faced a fundamental barrier that would sound familiar to any developer building with LLMs: non-determinism. Two identical prompts could yield different answers, especially once tool calls were involved. In markets, inconsistency compounds into loss.The problems with non-deterministic LLMs were specific:

• Execution couldn't be audited because the same input would produce different outputs, especially when tool calls were involved
• No verifiable records of what the agent actually did or why it made certain decisions
• Massive infrastructure overhead spent on guardrails and post-hoc debugging instead of shipping features

Previously, FereAI relied on conventional inference providers with seed settings and temperature hacks, supplemented by manual reviews, heuristic filters, and spot audits. This reduced variance somewhat but couldn't deliver true determinism or verifiable tool traces. The literature shows why this approach is fundamentally brittle: hardware differences, kernel variations, and runtime quirks all introduce unpredictability."When you're building a framework for long-term execution and dealing in real money, determinism becomes a key pillar," explains Akshaya Aron, Founder and CEO of FereAI. "EigenAI is the only solution today giving us deterministic, verifiable outputs — enabling our agents to perform tasks over months without us having to build guardrails that would've otherwise taken a year."With real capital and month-long strategies at stake, drift compounds into loss. Without determinism and verifiability, you can't confidently run agents for extended periods, expose APIs to third-party builders, or scale to meet institutional expectations. Consistency and verifiability are essential for turning experimental systems into production-grade infrastructure.

The Solution

FereAI found their answer in EigenAI's verifiable inference infrastructure.What is EigenAI?EigenAI, a service of the EigenCloud platform, provides a deterministic, verifiable LLM inference infrastructure designed to mitigate the inconsistencies and risks of opaque AI services. It addresses four critical vulnerabilities:

• Non-deterministic LLM inference - where identical inputs produce different outputs, making execution difficult to audit
• Prompt modification - which compromises carefully engineered context
• Response modification - which corrupts high-stakes agent actions
• Model modification - where providers might substitute less capable models to save costs

EigenAI commits to processing untampered prompts with untampered models and providing untampered responses, enabling programmable, trustworthy agent operations. It offers a familiar, low-latency, OpenAI-compatible API starting with gpt-oss-120b-f16, making integration straightforward for developers already familiar with standard LLM APIs. EigenAI recently launched on mainnet alpha.EigenAI achieves this through bit-exact deterministic execution of LLM inference on GPUs at scale. Given prompt X and model Y producing output Z, re-executing the same inputs will reliably produce output Z. Any discrepancy becomes cryptographic evidence of incorrect execution.Request early access to EigenAI at onboarding.eigencloud.xyz.How FereAI Implements EigenAI:The integration involved two key components:

• Routed critical inference paths through EigenAI's verifiable inference API
• Enabled agent primitives like reproducible tool call planning, ensuring every significant action has an auditable record

This infrastructure gave FereAI the foundation to shift from managing AI unpredictability to shipping new features.

Results

Since integrating EigenAI, FereAI has observed transformative impacts across development, reliability, and scale:Development Speed: Months of guardrail engineering work collapsed into days by relying on deterministic, verifiable outputs. External research confirms how challenging it is to achieve this independently.Product Reliability: The same prompt yields the same answer and the same trace, even as agents read news and social feeds and invoke tools. Users can reproduce outputs and understand exactly what happened, increasing trust and reducing support loops. This reproducibility is essential for the investment-grade analysis FereAI promises.Perhaps most significantly, the integration created a cultural shift within the team. As FereAI notes, "The real unlock is cultural: teams stop debating 'why did the bot say that this time' and start shipping. Determinism turns AI from inspiration to infrastructure."

Why Determinism Matters for AI Agents

The challenge FereAI faced isn't unique. Any developer building AI agents that handle real value (whether in DeFi, prediction markets, autonomous trading, or contract negotiation) confronts the same fundamental question: how do you trust a system that can't consistently reproduce its own behavior?Non-determinism in AI creates a trust barrier that keeps sophisticated AI capabilities trapped in demo mode. When an agent's decision-making process can't be audited or reproduced, it can't be trusted with real responsibility or real capital.EigenAI provides the verifiable infrastructure layer that enables AI agents to become reliable, auditable, and investable. For platforms like FereAI that need to operate at the intersection of AI sophistication and financial stakes, verifiable inference is foundational.When the base layer lacks determinism, everything built on top becomes unreliable. EigenAI positions verifiability as a first-class primitive, which is exactly what production agents need.Get early access to EigenAI. Build your own verifiable AI application at onboarding.eigencloud.xyz.