About ReputAgent
A single benchmark tells you how an agent performed once. Reputation tells you if you can trust it tomorrow.
The Core Insight
Evaluation is an event. Reputation is a story.
Think about how we trust people. You don't trust someone based on one reference call. You trust them based on a track record—consistent performance across different situations, over time, with evidence of growth and reliability.
Agents should work the same way. A benchmark score is like a single reference. Useful, but incomplete. Real trust comes from accumulated evidence: how does the agent perform across scenarios? How has it improved? Where does it consistently struggle?
That accumulated picture is reputation. And reputation is what should power trust decisions, access control, and governance in agent systems.
The Reputation Difference
"This agent scored 87% on the coding benchmark."
"This agent has completed 200 evaluations across 5 domains over 3 months."
"This agent is reliable for routine tasks, struggles with edge cases, but has improved 40% in the last month. Trust for Tier 2 workloads."
The Problem We Solve
Agent adoption is stalling because teams can't answer a simple question: "Can I trust this agent?"
"Over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls."
"About 90% of high-value AI use cases remain stuck in pilot mode... fewer than 10% ever make it past the pilot stage."
"A single compromised agent poisoned 87% of downstream decision-making within 4 hours."
"Prompt injection is the #1 vulnerability in LLM applications—and no fool-proof prevention method exists."
Why Benchmarks Aren't Enough
Benchmarks measure capability at a moment in time. They don't answer the questions that matter:
Built by Practitioners
ReputAgent is built by engineers who've deployed agent systems in production and learned—sometimes painfully—what it takes to make them trustworthy.
We've seen the 3 AM pages when agent pipelines cascade. We've debugged hallucination loops that cost real money. We've watched promising pilots stall because teams couldn't demonstrate reliability.
That experience shapes everything we build. We're not theorists—we're practitioners sharing what we've learned so you can build trust faster.
Connect With Us
Questions? Feedback? Found an error in our content? We genuinely want to hear from you. Reach out via our contact form.
Frequently Asked Questions
Common questions about ReputAgent and agent evaluation.
What is ReputAgent?
ReputAgent is a knowledge platform and toolset for building trust in AI agents. We provide documented evaluation patterns, failure modes from real deployments, synthesized research, and products like Agent Playground to help teams move beyond single benchmarks to reputation built on track record.
Who is ReputAgent for?
Teams building or deploying AI agents—ML engineers designing evaluation pipelines, product managers responsible for agent reliability, and enterprise teams establishing AI governance. Whether you're shipping your first agent or scaling a multi-agent system, our patterns and tools help you build trust systematically.
How do I get started?
Start with the Evaluation Patterns library to understand the building blocks. If you're debugging issues, check Failure Modes for documented problems and solutions. For personalized guidance, try the Agent Advisor. When you're ready to test, Agent Playground lets you stress-test agents against real-world scenarios.
Is the content free?
Yes. Our patterns, failure modes, research synthesis, and educational content are freely accessible. Agent Playground offers early access for teams who want hands-on evaluation tooling, and we offer consulting for teams who need expert guidance on evaluation strategy.
What is Agent Playground?
Agent Playground is our pre-production testing environment where you can stress-test agents against realistic scenarios before deployment. It generates evaluation data that builds toward a track record—moving beyond "it passed the benchmark" to "here's how it performs across conditions over time."
How is this different from benchmarks or leaderboards?
Benchmarks measure capability at one moment. Leaderboards rank agents by a single score. Neither tells you if an agent is reliable for your use case, how it handles edge cases, or whether it's improving. ReputAgent focuses on accumulated evidence across scenarios—reputation, not snapshots.
Why does agent reputation matter?
As agents interact with agents in multi-agent systems, trust decisions happen without human oversight for every interaction. Reputation enables automated trust: Agent A can check Agent B's track record before delegating work. This is how agent orchestration scales safely.
Can I contribute patterns or failures?
Yes. We welcome contributions from practitioners who've learned hard lessons deploying agents. If you've documented an evaluation pattern that works or a failure mode others should know about, reach out through our Contribute page or email us directly.
Our Approach
Three principles guide everything we build.
Failure-First
Understanding what breaks is the foundation for building trust. We document failure modes from real deployments so teams can learn from accumulated evidence.
Track Record
Our evaluation patterns are designed for repeated use, not one-time benchmarks. Each evaluation adds to the record. Reputation emerges from consistency.
Verifiable Evidence
Claims without evidence aren't reputation—they're marketing. We cite our sources. Every pattern links to research. Every agent's reputation should be backed by verifiable history.
Ready to Build Agent Reputation?
Start with the methodology, or dive into the tools.