Top 10 Guardian Agent Solutions to Evaluate in 2026

Comparing Available Vendors’ Features, Benefits, and Reviews

Why Active Supervision of AI Agents will be the key unlock in the year ahead  

If 2025 was the year that companies experimented with AI agents through prototyping, pilot projects, and early launches, 2026 is the year when agentic solutions start to become a strategic advantage and ROI driver for enterprises.

In order to make sure those agents are performing well for the business, companies must ensure there’s a solution that’s supervising and helping to continuously improve the value of those agents. Gartner calls this category of solutions “guardian agents” and predicts the category to capture as much as 15% of the AI market in the next four years. 

Essentially, Agents that supervise other agents are critical to ensuring the success of AI agents acting in the real world. Guardian agents, also called supervisor agents, serve the important purpose of monitoring, guiding, enforcing guardrails, and improving other AI agents and agentic workflows by factoring in monitoring, observability, security, policy compliance, brand protection, and performance optimization.

The risks of having lax or absent agent supervision are high, including financial and reputational consequences. A poor user experience can cause churn; communicate incorrect information; break workflows; violate brand standards; and lead to regulatory and compliance issues. 

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Expectations for an Agentic Supervisor Solutions

As you develop your evaluation criteria in order to shortlist potential providers and approaches for agent supervision, there are some key questions to consider:

• Is this only a technical monitoring solution? Such tools only tell you a small part of the picture, not the quality of the agent experience.
• Can I tailor the performance monitoring goals for each agent? Each agent you deploy has a unique purpose, and you should have an easy way to describe that agent’s goals and measure against those goals, as well as against your specific company policies and guidelines.
• Which humans are in the supervision loop? Some solutions are built to stay in the development/engineering team, while others empower the business and product owners to manage the agents for their unique functions.
• Can the system close the loop on improvements with minimal human work? There should be an easy path to take steps to optimize and improve the agent once an issue is identified. 

• How easily can I get information to support regulatory and compliance adherence? Look at the type of scorecarding, audit trailing, and reporting that’s available for the compliance side.
• What security protections exist for the supervisor agents? You want to make sure it’s not vulnerable to prompt injection attacks and other security risks. 
• What integration flexibility exists? Having MCP support (model context protocol) and other pre-built integrations will make it easier to plug in to a variety of environments and agents.
• Are there limitations to the agents the supervisor solution can work with? Ideally, the supervisor solution should be agnostic (independent) to work with any agents and provide a central view to all organization-wide agent performance.
  
  

The 10 AI Agent Supervision Providers Reviewed for 2026 

While the terrain for AI Agent Supervision is changing quickly, there are 10 companies that started building this solution throughout 2024 and 2025; these vendors are expected to expand their sizable lead in 2026.

The following vendors were reviewed: 

• Wayfound 
• LangChain’s LangSmith
• Vectara
• Credo 
• Salesforce Command Center
• ServiceNow AI Control Tower
• Arize
• Fiddler
• Galileo.ai
• Langfuse

Let’s now get to the review of each vendor.

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Wayfound (https://www.wayfound.ai)

‍The best overall closed-loop solution for enterprise-wide AI agent supervision used by business users to directly improve performance and reduce risk

• What they do: Centralized and agnostic platform built to be friendly for business users to proactively ensure their AI agents are continuously monitored, supervised, and optimized to meet business performance goals, not just technical performance.
• Notable: Wayfound was the first on the market to offer what Gartner now refers to as a ‘guardian AI Agent’ that’s also accessible to non-technical users and is followed by their analysts 
• Who it’s best for: Mid- to large-sized enterprises who are deploying AI agents built on any framework or model, and where a business user needs to know, prioritize, and act on agent performance without burdening engineering for each improvement instance.
• Advantages:
  
  • Designed to empower the business user to take more control over agent performance and improvements, without requiring technical help, while giving the engineering team a low-code experience for integrations and not having to read LLM logs and traces. 
  • Automated approach to agent supervision is both preventative and closed-loop, by learning from history and proactively adding guardrails 
  • Integrates with all agent builder frameworks: vendor agnostic
  • Has its own Model Context Protocol (MCP) server to integrate easily with AI agents and LLMs – three lines of MCP configuration gets you supervision, self-healing, and optimization
  • Native integration with Salesforce Agentforce, as an official partner
  • Monitors agents inline during AI runtime as well as offline
  • Visual interface and email alerts make it easy to see and understand agent knowledge gaps, action failures, user satisfaction outcomes, and compliance with guidelines/guardrails.
  • Benchmarking of all agents (anonymized) managed by Wayfound across all clients, so that users can see how their agent performance compares to others
  • Sandboxing and testing features let you learn and optimize before moving new agents or updates to guidelines and agents to production
  • Support for multi-agent workflows
  • Provides recommendations on how to improve agent performance, which speeds iteration of AI agents at any phase
• Features you’ll like:
  
  • Easy-to-consume scorecards, dashboards, reports with benchmarks, and improvement suggestions
  • Daily and instant alerts that keep you focused on actions to take now vs. navigating a data deluge
  • No need to read through LLM logs and traces
  • Visual mapping of all the AI agents used across your organization
  • Low-code ease to hook up to existing agents and environment, thanks to API and MCP integration options
• Shortcomings:
  
  • Does not yet provide monitoring of model costs
  • Does not yet track token consumption 
• Benefits cited:
  
  • 80% decrease in AI agent supervision costs
  • Speeds up AI agent deployments by 300% 
  • 30% faster human-in-the-loop approvals
• Customer feedback: Customer case studies and presentations are available to view. Customer Sauce Labs using Wayfound for customer support agents said: “Once we gave Wayfound to our customer support team, we really started to see massive value” and “The results are super interpretable, highly searchable, and auto-tagged… we get a lot of value out of it really quickly and in a highly human-readable format.”
• Integrations: MCP, API, Salesforce Agentforce, Langchain, CrewAI, Intercom, Snaplogic, Amazon, Google, OpenAI, Anthropic, Nvidia, OpenTelemetry and more
• Pricing: Wayfound starts at $179 per month for supervising one AI agent, but really prices for the Enterprise at $749 per month for 5 AI agents, tiering up from there

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‍LangChain’s LangSmith (https://www.langchain.com/langsmith)

• What they do: Unified building and observability platform for development teams to design, monitor, trace, debug, and test the performance of AI agents and LLM-based applications
• Notable: LangSmith is the commercial observability and monitoring offering built on the open LangChain framework. Announced wait list for an upcoming no-code builder in October 2025 to make agent design possible to more than just developers. 
• Who it’s best for: AI agent and application development teams, especially those using the LangChain framework (although it does support other frameworks)
• Advantages:
  
  • Insight Agent automatically analyzes production traces to categorize agent behaviors and help identify issues
  • Includes full inputs and outputs, to datasets where you can create and apply an evaluation to test LLM responses, while also collecting human feedback on the assessments
  • Provides a prompt “playground” environment to create, test, compare, and iterate prompts
  • Monitoring view allows you to use either pre-built dashboard components or create your own to track key AI metrics including costs, performance latency, and response quality
  • Option to self host if you purchase the Enterprise-level package
• Features you’ll like:
  
  • The playground feature to experiment with prompts and preview the outcomes before moving updates to production
  • When needed, stores detailed tracing with full access to all details of each response and action 
• Shortcomings:
  
  • While their Insight Agents tells you what went wrong, it does not automatically prevent future issues in production, identify knowledge gaps and make improvement recommendations
  • Does not provide automatic scorecarding and benchmarking of agent performance
  • No business-friendly views to the monitoring insights; purely developer focused
  • Does not yet seem to have its own model context protocol (MCP), as of August 7, 2025, but reports on third-party developer sites suggest it is currently under development
• Benefits cited: No specific metrics for LangSmith cited, other than general benefit statements about increased speed and confidence in deploying agents 
• Customer feedback:Difficult to find feedback specific to LangSmith on their G2 reviews. A customer case study from Klarna cited speeding average customer query resolution time by 80%, while another customer Podium reduced the need for engineering intervention in support issues by 90%. A number of other LangSmith customer case studies can be found on their website.
• Integrations: No full integration list provided on their website, but they use a standard OpenTelemetry client, can integrate with the open LangChain framework and most other frameworks. 
• Pricing: Has a free “solo” developer entry point that involves monthly pay-as-you-go billing, but true business-ready pricing starts at Developer Plus at $39 per month for up to 10 seats.

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Vectara Guardian Agents (https://www.vectara.com/business/solutions/use-cases/guardian-agents) 

• What they do: Platform for enterprise conversational AI and agentic RAG use cases that includes a “guardian agent” capability for AI monitoring and governance
• Notable: Very focused on accuracy and reducing hallucinations
• Who it’s best for: Larger-sized enterprises across a range of industries (finserv, telco, education, manufacturing) deploying conversational AI agent chatbots, especially in customer support uses
• Advantages and features you’ll like: 
  
  • Hallucination Corrector is looking for hallucinations in real time to identify them, suggest fixes, and improve guardrails
  • Governance Control Pane designed to help enterprises scale-up guardrails across multiple agents
• Shortcomings:
  
  • No mentions of agent performance benchmarking, scorecarding, and real-time alerting
  • Frankly, a bit light on details on their website as it relates to their guardian agent / AI agent governance solution
  • Appears to be designed for developers and engineers vs. to empower the business and product owners to handle front-line / first-line agent performance supervision and improvement
• Benefits cited: No specific hard benefit metrics found on website 
• Customer feedback: Very light on customer stories on their website, none which specifically focus on the guardian agent capabilities 
• Integrations: A handful of pre-built integration partnerships are listed on their website
• Pricing: Free 30-day trial, but then base pricing starts at $100,000 per year for 1 SaaS-based deployment and up to 10 million annual credits

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Credo (https://www.credo.ai/)  

• What they do: AI governance and regulatory compliance-focused platform combined with related expert advisory services aimed at enterprise-wide AI usage.
• Notable: Industry analyst recognitions include being named to Gartner Cool Vendor 2025 in Cybersecurity and Forrester Wave Leader for 2025 AI Governance.
• Who it’s best for: Mid- to Large-sized businesses, especially those in highly regulated industries, who need to document, audit, and be ready to report on AI application usage and compliance.
• Advantages:
  
  • The solution centralizes analysis and insights on how to stay compliant with new and changing government regulations of AI, including from third-party AI tools. Examples given on their website. 
  • Newly launched Shadow AI feature to help identify where employees are accessing AI tools that haven’t been through the established review process
  • Centralized AI Registry lets companies inventory and priority-rank all AI use cases across the business (and their metadata), using familiar project management tooling
  • Policy Packs feature help standardize, apply, and track AI solutions’ adherence to business goals, GenAI guardrails, and regulations
  • Generates governance report cards to share with executive stakeholders, customers, and regulators
  • Can support public, private, or hybrid cloud or self hosted environments
• Features you’ll like:
  
  • Integrates with your model store to auto-detect new AI uses that need governance
  • Automatically suggests which Policy Packs to apply to each AI use case
• Shortcomings:
  
  • When it comes to AI supervision, it does not cover goal-based performance and outcomes of AI agents. That means it does not look at AI knowledge gaps, user feedback, action failures, goal conversions, and other performance-related metrics
  • Does not provide benchmarking along business performance measures
  • Does not suggest performance-related changes to make to AI apps, outside of what’s required to stay in compliance with regulations and strict business rules
  • Does not yet seem to have its own model context protocol (MCP), as of August 7, 2025
• Benefits cited: No specific benefit ROI metrics cited on the website, but key value messages promoted are on “trust” and “safety.”
• Customer feedback: Several enterprise-grade customer and strategic partner logos scroll across their website, with a few in-depth case studies available for more detail. Mastercard is one, with their Chief Data Officer cites Credo’s value to help them speed, scale, and track GenAI across their global business. 
• Integrations: Not many specifics given, and no obvious access to documentation is available, but says they provide native integrations with AI systems for a ‘single pane of glass’
• Pricing: No information about pricing or tiers available on their website

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Salesforce Agentforce Observability (fka Command Center) (https://www.salesforce.com/agentforce/observability/) 

• What they do: Part of their all-in-one build-test-deploy-iterate Agentforce Studio suite, this module is for supervising, observing, and improving AI agents
• Notable: GA announced in November 2025 its capabilities for agent analytics and optimization. Covers only Agentforce agents, but Salesforce does have a partnership with Wayfound for also capturing third-party agents and to provide independent agent supervision
• Who it’s best for: Salesforce customers with AI initiatives, looking to get as much as they can from a single vendor and without a high additional agent supervision burden on development teams
• Advantages: 
  
  • Performance metrics tracked are specific to each agent (i.e. different measures for a sales AI agent vs. a customer support agent)
  • Salesforce-family UI experience, so designed more for the business user vs. developer/engineer viewpoint (although you can drill down to tracing specifics of each agent interaction)
  • Can take insights and go back to the Builder module to add new instructions, test to make sure it’s fixed the issue, so that you can improve agents
  • Call up an agent in the app to help you navigate and make sense of what you’re seeing
• Features you’ll like:
  
  • Includes ROI metrics based on AI consumption costs
  • Agent interactions can be tagged for easy sorting and filtering
  • Can choose which agents you or certain persona roles view, based on relevance and priorities
• Shortcomings:
  
  • Agent proactive health monitor capabilities are not available yet, with estimated timeframe of Spring 2026
  • Covers Agentforce agents, with third-party agent management handled through addition of an Agentforce partner like Wayfound
  • Does not provide capabilities for specifically preventing, identifying, and recommending fixes to agent knowledge gaps (requires manual human search into each interaction’s details)
  • Does not provide benchmarking of agent performance
  • Does not enable agent self-healing and automated closed-loop performance improvements
• Benefits cited: They have added a self-service ROI calculator to their website so you can estimate benefit value based on type of agent use case
• Customer feedback: Salesforce has dozens of Agentforce customers now cited on its website,  some with testimonials and case studies. Hard to find anything specific to the Observability solution, which may be due to still being early in its GTM phase.
• Integrations: Data can be automatically integrated with other Salesforce products, including Service Cloud and Sales Cloud. Promoting its leverage of MCP servers that you can find within Salesforce’s partner ecosystem to connect to their own MCP client.
• Pricing: Lots of flex credit nuance around pricing as described on their website, but none of the pricing specifically mentions Observability

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ServiceNow AI Control Tower (https://www.servicenow.com/products/ai-control-tower.html) 

• What they do: Centralized view within the ServiceNow AI Platform for supervising, managing, governing, and securing AI agents
• Notable: Selected by Microsoft to integrate ServiceNow Control Tower with Agent 365 to handle the supervision of any AI agent in Microsoft Foundry and their Copilot Studio environments 
• Who it’s best for: Those seeking enterprise-grade compliance and accountability from AI initiatives
• Advantages: 
  
  • Works with any AI agent, whether built internally or from third parties
  • AI project management features to help prioritize against business goals
  • Check AI projects against your security standards
  • Underpinned by ServiceNow’s Configuration Management Database and unified data architecture
  • Embedded with the ServiceNow AI Platform’s other modules for building AI agents and automated workflows
• Features you’ll like:
  
  • Versatility to work with mix of in-house and third-party AI agents
  • ROI analysis for agents
• Shortcomings:
  
  • Does not appear to address automatic surfacing and alerting of agent knowledge gaps, prevention, and closed-loop self healing 
  • Does not appear to offer agent performance benchmarking
  • Does not appear to provide automatic agent-driven recommendations on how to improve the agents it’s supervising and the context of why those recommendations are being made 
• Benefits cited: No specific hard benefit metrics yet available on their website or related announcement materials for Control Tower, aside from a messaging emphasis on maximizing AI ROI and enabling seamless integrations 
• Customer feedback: Nothing yet specific to the Control Tower, but there are some customer quotes included in the launch press release that speak generally to ServiceNow’s AI offerings and vision. They also have the Microsoft announcement with a general statement from an Agent 365 executive. 
• Integrations: No specific details provided outside of the Microsoft Agent 365 partnership
• Pricing: Request for pricing

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Arize (https://arize.com/)

• What they do: An all-in-one open source platform that spans the lifecycle of building, testing, deploying, monitoring, and iterating LLM AI agents 
• Notable: The Arize AX offerings are for enterprise usage, while their Phoenix version is an open source tool for LLM tracing and observability
• Who it’s best for: Open-source-favoring engineers and developers responsible for the entire lifecycle from building to monitoring LLM-based AI apps and agents, particularly at larger B2C companies (although it does have B2B clients)
• Advantages:
  
  • Build your own evaluators to monitor agents and enabling CI/CD where issues can be detected early
  • Co-pilot agent helps you de-bug traces or spans, using OTEL open standards
  • Set up a playground for running prompt and dataset experiments, comparisons and replays to identify vulnerabilities, and also to model drift
  • Humans can annotate alongside findings
  • Monitoring can include custom metrics, alerting thresholds and dashboards
  • Automated root-cause analysis workflows allows for deeper explainability context
  • Run UMAP comparisons for similarity search to find and analyze clusters of data points that look like a reference point of interest
  • Has its own MCP server for ease of interoperability
• Features you’ll like:
  
  • You can search for specific data points and then drill down from there
  • Heatmaps let you visually pinpoint and prioritize model performance issues
• Shortcomings:
  
  • UX does not make the monitoring insights consumable for business or product owners of AI apps, but built for engineers. Reviews on G2 cite cons around learning curves, manual instrumentation, and complexity of navigating the tool.
  • Does not specifically analyze and suggest recommendations to fill knowledge gaps in agents
  • Does not provide performance benchmarking data for agents
• Benefits cited: Benefit statements are available in some key case studies on their website, but most are light on quantified metrics and ROI. One customer example reported it deployed 15 LLM use cases in six months; 
• Customer feedback: Says they have 5 million downloads per month, with many well-known B2C brand logos on their website, including Pepsi, Priceline, Reddit, DoorDash, Roblox, Instacart, and Air Canada. Customer quotes cite the benefit of including observability from the moment agents are built and ease of debugging, and a number of customer case studies are available on their website. They also held a customer conference event in 2024.
• Integrations: Built on top of OpenTelemetry. A wide range of integrations for Arize Phoenix can be found in their documentation, falling into the categories of tracing (i.e. OpenAI, LangChain, Vercel AI SDK, Amazon Bedrock, etc.), eval models, and eval libraries 
• Pricing: Arize Phoenix is available for free as an open source product, and from there the Arize AX enterprise offerings start as a free option for a single developer (with other constraints) and tier up with additional enterprise-level features starting at $50 per month for up to 3 users. Most large enterprises will likely be getting custom pricing.

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‍Fiddler (https://www.fiddler.ai)

• What they do: Offers a central command center for AI lifecycle observability and security for all of an enterprise’s LLMs and AI agents, checking guardrails and detecting anomalies. 
• Notable: Fiddler Trust Service is made up of Fiddler Trust Models, designed for exceptionally fast and accurate task-specific scoring of LLM prompts and responses, in order to identify risks such as hallucinations, toxicity, and prompt injection attacks.
• Who it’s best for: Developer-focused solution for enterprise-level agentic and LLM deployments in SaaS, virtual private cloud (VPC) and AWS GovCloud environments
• Advantages:
  
  • Integrations with a variety of model frameworks and data sources for ingestion
  • Centralized monitoring for developers
  • Explainability (local and global) in human-understandable terms about issues such as data drift and outliers, and you can bring your own explainers
  • Intersectional fairness metrics can be accessed to reduce bias
  • Track multi-agent hierarchy, interactions, workflows, and decision paths to find cross-agent dependencies, bottlenecks, and failure points
  • Access past, present and future model outcomes to improve debugging and performance
  • 80+ metrics and ability to connect to your business KPIs
  • Audit reports to meet governance and compliance standards
• Features you’ll like:
  
  • A 3D UMAP visualization that lets engineers explore data, as in-depth as desired, to isolate problematic prompts and responses
  • Can customize dashboards and reports to highlight the LLM metrics that matter most to your business KPIs
  • Can roll back models, data, and even code to reproduce predictions and determine if bias was involved
• Shortcomings:
  
  • Because of the tool’s focus on developers, there is no business-friendly UI experience, making it difficult to discover, understand, prevent, and prioritize performance issues, especially across multiple agents 
  • The product UX may feel overwhelming with its inclusion of many different reporting modules 
  • Does not provide suggestions for AI agent improvement
  • Does not deliver agent benchmarking insights
• Benefits cited:
  
  • <100 ms latency in Fiddler
  • 7-18X reduction in computational overhead compared to publicly available data sets
  • 50% more accuracy when compared to publicly available data sets
• Customer feedback: They list several customers, both by name and anonymized on their website (https://www.fiddler.ai/customers) including IAS, Lending Point, Tide, and a unit of the US Navy. Customers cite their strengths in observability and monitoring in the background.
• Integrations: Cited logos include Amazon SageMaker, Amazon Bedrock, Python, H2O, TensorFlow, OpenTelemetry, LangGraph, XG Boost, Scikit Learn, Snowflake, SingleStore, Amazon S3, Amazon Red Shift, PostgreSQL, Google Big Query, Nvidia, Google Cloud, Carahsoft, Databricks, Domino, Datadog and more
• Pricing: No information available on their website, requires a query to their sales team.

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Galileo (https://galileo.ai/) 

• What they do: Solution that automates online and offline evaluation of AI accuracy by testing features, prompts, workflow handoffs, and models to identify failures and guardrail issues.
• Notable: Their approach is based on unit testing and CI/CD (continuous integration and continuous delivery/deployment) to deliver a combination of low latency, accuracy, and low cost.
• Who it’s best for: Any AI project at mid- to large-sized enterprises in both B2C and B2C, providing an interface that, even though developer-oriented, is also fairly easy to consume by non-technical users and AI product owners.
• Advantages:
  
  • Have their own proprietary models called Luna to deliver much lower latency compared to other models while keeping costs low for always-running AI evaluations. Also helps with hallucination detection.
  • Provides out-of-the-box AI agent evaluators and their own MCP to handle specific analytics and metrics reporting, such as RAG metrics, Safety metrics, Security metrics, and more. Four new metrics were added in October 2025 to capture agent flow, efficiency, conversation quality, and intent.
  • You can also build custom evaluator agents to meet needs that their pre-built ones don’t cover.
  • Observability is done in real-time in production, while capturing detailed traces 
  • Reports on latency and costs
  • Recommends fixes and actions to take when failures or risks are identified
  • Provides a playground UI to run and compare various test sets
  • Built to handle multi-agent systems and workflows
• Features you’ll like:
  
  • Prompts can be refined using ML help and with versions organized in one place
  • Auto-tune allows for incorporation continuous learning with human feedback (CLHF)
  • Graphical reporting shows the at-a-glance path an agent takes to see where it goes off course
• Shortcomings:
  
  • To gain the low-latency benefits, you must fine-tune a model using a significant amount of training data, driving up costs and error risks
  • No benchmarking of agent performance metrics so you can compare your agent performance (although they do maintain an agent leaderboard tracker for 30 LLM models, where you can submit your own agent to see how each of the models perform and benchmark)
  • No clear measure and identification of AI knowledge gaps that can be remedied or made self healing
  • Developer-focused and not really usable by business-side owners
• Benefits cited:
  
  • Eliminate 80% of manual evaluation time
  • Ship iterations 20% faster
  • Less than 200ms latency to catch prompt attacks, hallucinations, and data leaks
• Customer feedback:
  
  • Some sizable enterprise customer logos including HP, John Deere, and Comcast. Several anonymized case studies are available on their website, with the main value cited around significant reductions in time to spot and resolve issues.
• Integrations: Integrations via their SDKs and API, connecting to all major models, orchestration tools, retrieval tools, and cloud environments 
• Pricing: Has a free Developer starter offering, while Enterprise pricing requires an inquiry to their sales team

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Langfuse (https://langfuse.com/) 

• What they do: Open source, framework-agnostic platform to observe, monitor, trace, debug, and improve any LLM application
• Notable: Widely deployed open source observability and debugging tool generally favored for smaller-scale businesses and apps
• Who it’s best for: Open-source development team users who need to drill-down into observability and analytics for LLM-powered apps in production, especially built around detailed tracing
• Advantages:
  
  • Provides a developer view of logging, tracing, prompt versioning, user feedback collection, and evaluation tooling – added new feature updates on October 2025 to make complex agents easier to understand and debut
  • Create custom evaluation templates to apply to traces
  • Create your own custom annotations to apply
  • Prompt management capabilities let you version control, review, edit, publish, and rollback prompts within the tool, including running new prompt experiments against test datasets
  • Includes data around latencies and model costs
  • Agnostic, works with LangChain, LlamaIndex, or even a custom-built LLM stack. Has its own model context protocol (MCP)
• Features you’ll like:
  
  • Toggle easily between tree and timeline views in the UX
  • Side-by-side prompt comparison playground views for experimentation, testing, and collaborative evaluation 
  • Easily change time period views in dashboard components
  • Deploy to cloud or self host
  • Collaboration features
• Shortcomings:
  
  • Because it’s a developer-first tool, there is not really a business user-friendly view into the monitoring and outputs of the solution, which maintains burden on the development and support team to investigate and resolve even smaller issues with AI agents and models 
  • Does not really recommend or suggest where to prioritize or improve, just presents the data for you to interpret
  • Does not provide self-healing and preventive capabilities to automate the mitigation of future agent issues
  • Good at applying its capabilities to a single LLM app or agent, but harder to get an enterprise-wide picture across all agents and apps
  • Does not identify model knowledge gaps, no benchmarking, and no performance scorecarding
• Benefits cited: No specific measured benefits or ROI statements published on their website  
• Customer feedback:
  
  • Positive feedback published on their website from their user community, with fans for its open source and level of details for developers to respond to support requests.
  • They claim 14 million SDK installs per month and 6 million Dokker pulls as of November 26, 2025.
  • Over 18,000 GitHub stars also as of that same date.
• Integrations: Based on OpenTelemetry, with dozens of ready integrations with AI model frameworks and model providers, along with other direct integrations. List is published in their documentation. 
• Pricing: Their Hobby-level (proof of concept) plan lets your get started for free, but true production-ready deployment pricing starts at $59 per month for unlimited users and tier up from there

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In Summary and Recommendation

AI agent supervision, aka ‘guardian agents,’ are now an analyst-validated need for enterprises growing their agentic strategy. This will be a fast-evolving area, but those getting control of agent performance, compliance, reliability and safety sooner than later will be better guarded to protect their business and maximize their benefits from AI.

In our review of leading platforms, we believe that Wayfound is the standout for enterprise-wide AI agent supervision that’s business-user friendly while still supporting developer needs. With real-time performance scoring, actionable improvement recommendations, and deep integrations thanks to its own MCP—and including as an official Salesforce Agentforce partner—Wayfound shows how guardian agents can turn AI oversight from a challenge into a competitive advantage, without burdening the organization.

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How We Compiled This Information

This guide to the AI agent supervision solution landscape was compiled using a number of online sources, including first-party sources.

Each vendor’s own website was reviewed, including any available product demo videos, product screenshot images, pricing information, product data sheets, product and media announcements, blogs, and customer testimonials and stories. Product documentation was also reviewed where helpful.

G2 as a crowd-sourced review resource provided insights about overall ratings and reviews where available. Independent developer websites, news articles and press coverage, general online searches, and AI search overviews helped to fill some gaps in information available on vendor websites. These were not weighted as heavily in our assessment for authority.