Coval
Coval is a simulation and evaluation platform designed to accelerate the development of reliable AI agents across chat, voice, and other modalities. By automating the testing process, Coval enables engineers to simulate thousands of scenarios from a few test cases, allowing for comprehensive assessments without manual intervention. Users can create test sets by adding customer transcripts or describing user intents in natural language, with Coval handling the formatting. The platform supports both text and voice simulations, facilitating the testing of AI agents against a set of scorecard metrics. Comprehensive evaluations of agent interactions are provided, enabling performance tracking over time and root cause analysis of specific runs. Coval also offers workflow metrics that provide observability into system processes, aiding in the optimization of AI agents.
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Maxim
Maxim is an agent simulation, evaluation, and observability platform that empowers modern AI teams to deploy agents with quality, reliability, and speed.
Maxim's end-to-end evaluation and data management stack covers every stage of the AI lifecycle, from prompt engineering to pre & post release testing and observability, data-set creation & management, and fine-tuning.
Use Maxim to simulate and test your multi-turn workflows on a wide variety of scenarios and across different user personas before taking your application to production.
Features:
Agent Simulation
Agent Evaluation
Prompt Playground
Logging/Tracing Workflows
Custom Evaluators- AI, Programmatic and Statistical
Dataset Curation
Human-in-the-loop
Use Case:
Simulate and test AI agents
Evals for agentic workflows: pre and post-release
Tracing and debugging multi-agent workflows
Real-time alerts on performance and quality
Creating robust datasets for evals and fine-tuning
Human-in-the-loop workflows
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Vivgrid
Vivgrid is a development platform for AI agents that emphasizes observability, debugging, safety, and global deployment infrastructure. It gives you full visibility into agent behavior, logging prompts, memory fetches, tool usage, and reasoning chains, letting developers trace where things break or deviate. You can test, evaluate, and enforce safety policies (like refusal rules or filters), and incorporate human-in-the-loop checks before going live. Vivgrid supports the orchestration of multi-agent systems with stateful memory, routing tasks dynamically across agent workflows. On the deployment side, it operates a globally distributed inference network to ensure low-latency (sub-50 ms) execution and exposes metrics like latency, cost, and usage in real time. It aims to simplify shipping resilient AI systems by combining debugging, evaluation, safety, and deployment into one stack, so you're not stitching together observability, infrastructure, and orchestration.
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Plurai
Plurai is the real-world trust platform for AI agents, built for simulation-driven evaluation, protection, and optimization that turns agents into trusted, continuously improving production systems. It helps teams train evals and guardrails tailored to their use case, bridging the gap from prototype to reliable production at scale. Plurai’s simulation platform prepares agents for the real world, not the lab, with hyper-realistic, product-tailored experimentation and evaluation that covers production complexity. It generates authentic multi-turn scenarios, personas, required artifacts, and tool mocking, using organizational PRDs, relevant sources, and policies to build a knowledge graph and expand edge-case coverage. Instead of relying on static datasets, manual test creation, or inconsistent LLM-as-a-judge methods, Plurai groups evaluations into structured, runnable experiments so teams can test new versions, measure regressions, and validate improvements before release.
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