Basalt Agents

Product Introduction

1. Basalt Agents is an end-to-end AI observability platform designed to prototype, test, and deploy complex AI workflows composed of multiple prompts and scenarios. It enables teams to build AI agents through a no-code interface, run them against datasets, and evaluate performance using automated tools. The platform integrates evaluation, monitoring, and iterative improvement into a unified workflow for AI development.
2. The core value of Basalt Agents lies in its ability to ensure AI reliability by automating quality checks, enabling rapid iteration, and providing enterprise-grade monitoring for production deployments. It reduces development risks by validating AI workflows at every stage, from prototyping to live deployment, while fostering collaboration between technical and non-technical teams.

Main Features

1. AB Test Workflows: Users can A/B test entire agentic chains directly from their codebase, compare results across multiple versions, and evaluate performance using predefined metrics. This feature supports parallel testing of prompts, models, and logic flows to identify optimal configurations.
2. AI-Powered Evaluators: The platform includes 50+ prebuilt evaluator templates that automatically detect errors, inconsistencies, or quality deviations in AI outputs. Teams can also create custom evaluators using natural language instructions or code-based rules.
3. AI Co-Pilot for Iteration: Basalt Agents provides real-time suggestions for prompt improvements, model selection, and workflow optimizations based on test results. The co-pilot analyzes performance data to recommend adjustments, such as refining instructions or switching LLM providers.

Problems Solved

1. Unreliable AI Outputs: Basalt Agents addresses the challenge of maintaining consistent quality in AI-driven features by automating evaluations and enabling granular monitoring of agent behavior. It prevents deployment of flawed logic or poorly performing prompts.
2. Cross-Team Collaboration Barriers: The platform serves product managers, developers, and domain experts working on AI projects, providing tools for shared iteration, version control, and centralized feedback.
3. Production Risks: Teams use Basalt Agents to validate workflows against edge-case scenarios, monitor live deployments for regressions, and receive alerts when predefined error thresholds are breached.

Unique Advantages

1. End-to-End Integration: Unlike standalone evaluation tools, Basalt Agents combines prototyping, testing, deployment, and monitoring in a single platform, eliminating the need for disjointed tools.
2. No-Code and Code-First Flexibility: Non-developers can build and test agents via a visual interface, while developers leverage an SDK for programmatic integration, versioning, and CI/CD pipeline compatibility.
3. Enterprise-Grade Security: The platform adheres to strict data privacy standards, ensuring prompt and test case confidentiality through encryption, role-based access, and compliance with SOC 2 frameworks.

Frequently Asked Questions (FAQ)

1. How does Basalt ensure AI agents perform reliably in production? Basalt Agents runs automated evaluations using AI and rule-based checks at every development stage, monitors live usage metrics, and triggers alerts for anomalies like hallucination rates or response latency spikes.
2. Can non-technical team members contribute to AI agent development? Yes, the no-code playground allows product managers and domain experts to prototype prompts, review test results, and provide feedback without writing code.
3. What security measures protect sensitive data used in testing? All data is encrypted in transit and at rest, with optional on-premises deployment and granular permissions to restrict access to prompts, test cases, and evaluation results.
4. How does the AI co-pilot improve prompt engineering? The co-pilot analyzes historical performance data to suggest prompt restructuring, optimal LLM parameters, and alternative phrasing, reducing manual trial-and-error cycles.
5. Does Basalt support multi-model deployments? Yes, users can test and deploy agents across multiple LLM providers (e.g., OpenAI, Anthropic, Mistral) and compare cost, speed, and accuracy metrics side-by-side.