BotGauge
AI Agent Red-Teaming & Evaluation Platform.
A startup from de, the United States, founded by Pramin Pradeep, Vivek Nair, Naresh , Sreepad.
AI Agent Red-Teaming & Evaluation Platform.
A startup from de, the United States, founded by Pramin Pradeep, Vivek Nair, Naresh , Sreepad.
This page is designed to help you find out whether BotGauge is good and if it is the right choice for you.
BotGauge helps teams red-team, evaluate, monitor, and govern AI agents from development to production.
Agents call tools, touch sensitive data, and act with real autonomy, which means they fail in ways traditional checks were never built to catch. Prompt injection hidden in a document, a tool call nudged outside its intended scope, a multi-step reasoning chain steered into an unapproved outcome: these failures rarely show up from asking an agent a few sample questions.
BotGauge runs adaptive red-team campaigns against your live agent to surface these exact risks: prompt injection, unauthorized tool calls, data leakage through connected systems, and guardrail bypasses. Every finding becomes a permanent evaluation, added to your agent's regression suite so the same failure can't silently reappear in a future prompt tweak or model update.
Monitoring keeps watching after deploy, flagging drift and recurring failure patterns as models and tools change. Governance turns technical findings into clear, evidence-based guardrails and insight that engineering, security, and compliance stakeholders can actually act on, built from real attacks that worked, not generic policy templates.
BotGauge works with the frameworks teams are already shipping with, including LangGraph, CrewAI, AutoGen, and the OpenAI Agents SDK, plus MCP-connected agents. It's vendor-neutral and framework-agnostic by design, built to plug into your existing stack rather than lock you into one ecosystem.
Built for AI and ML engineering teams running agents in production who need ongoing red-teaming, durable evals, monitoring, and governance, not a one-time review.
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Adaptive Red-Teaming
Runs adversarial campaigns against your live agent using adaptive attack sequences rather than a static library of known jailbreak strings, probing for prompt injection, tool-call hijacking, and policy bypasses that only surface under sustained, evolving pressure.
Multi-Turn Attack Simulation
Simulates extended conversational context to catch failures that only emerge several turns into an interaction, where an agent's reasoning chain gets incrementally steered toward an unauthorized action through seemingly benign intermediate steps.
Prompt Injection Detection
Tests for both direct and indirect prompt injection, including payloads embedded in retrieved documents, tool outputs, and third-party context the agent ingests mid-task, not just injection attempts in the user-facing prompt itself.
Unauthorized Tool-Call Discovery
Identifies conditions under which an agent can be manipulated into invoking tools outside its intended scope, chaining tool calls in unintended sequences, or triggering actions with elevated privileges it shouldn't have access to.
Data Exfiltration Probing
Attempts to extract sensitive data through an agent's connected integrations, testing for leakage paths across API calls, database queries, and downstream tool responses that bypass expected data-handling boundaries.
Automated Regression Evaluation
Converts every red-team finding into a permanent evaluation added to the agent's regression suite, so previously discovered vulnerabilities are automatically re-checked on every prompt revision, model version change, or framework upgrade.
Post-Deploy Monitoring
Continuously monitors deployed agents for recurrence of previously identified failure modes and for behavioral drift introduced by upstream model updates, prompt changes, or new tool integrations.
Guardrail Governance Reporting
Surfaces structured, release-over-release reporting on guardrail coverage, open risk gaps, and remediation status, built for engineering, security, and compliance stakeholders who need auditable evidence, not raw logs.
Vendor-Neutral Architecture
Operates independently of any single model provider or agent platform, designed to integrate alongside existing observability and evaluation tooling rather than requiring exclusive adoption.
mid-market SaaS companies shipping customer-facing agents
Most tools in this space specialize in one slice: evaluation, or observability, or red-teaming, rarely all four working together as one loop. BotGauge treats red-teaming, evaluation, monitoring, and governance as a continuous cycle rather than separate products bolted together: every adversarial finding automatically becomes a permanent evaluation, so an agent's protection compounds over time instead of resetting with every audit. It's also vendor-neutral and framework-agnostic by design, built to plug into LangGraph, CrewAI, AutoGen, and the OpenAI Agents SDK rather than requiring a migration to a proprietary platform.
Most competitors in AI agent evaluation and observability are either being absorbed into much larger platforms or were built for LLM calls generally, not the specific failure modes of autonomous, tool-calling agents. BotGauge is purpose-built for agent-specific risk: prompt injection through tool outputs, unauthorized tool-call chaining, multi-turn reasoning manipulation. It's also independent, not bundled into a larger company's broader commercial roadmap, so teams aren't betting their agent security on priorities set by a much bigger acquirer.
AI and ML engineering teams building and shipping production agents, from individual engineers standing up their first tool-calling agent to platform teams standardizing red-teaming and evaluation across an entire organization. It's built for people who need a defensible, evidence-based answer to "how do we know this agent is safe," not a theoretical policy checklist.
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