Top 9 Solutions for Supervising AI Agents in 2025

Features, Benefits, Reviews

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Introduction to Solutions for Monitoring and Supervising Agentic AI

The rapid rise of AI agents is transforming how businesses operate—but it’s also creating a new layer of complexity. Organizations are deploying agents across sales, support, operations, and product workflows at a speed that far outpaces traditional oversight methods.

As adoption widens, so does the complexity of keeping these agents accurate, effective, compliant, and aligned with business goals. Gartner calls this emerging category “guardian agents”—AI designed to monitor and improve other AI agents and agentic workflows —operating at the intersection of security, observability, monitoring, orchestration, and performance optimization.

Without this layer of oversight, organizations face serious risks: agents can hallucinate, overlook key knowledge, misrepresent information, fail at workflows, leak sensitive data, or even violate regulatory requirements. These issues can hurt customer experiences, business functions, and even have financial penalties. 

AI agent supervision is no longer optional—it’s the key to scaling AI responsibly while maximizing ROI. It also enables businesses to move forward with optimism and confidence in applying AI, reaping the benefits rather than fearing the consequences.

Challenges and Opportunities Addressed by AI Agent Supervision

Effective AI agent supervision and guardianship platforms can:

• Improve performance against business goals by increasing outcome success rates.
• Detect and close knowledge gaps so agents have the right information at the right time.
• Reduce inaccurate outputs (hallucinations) and task failures.
• Maintain regulatory and compliance adherence while preventing data leaks or jailbreaks.
• Protect against prompt injection attacks and other security risks.
• Provide centralized visibility into every AI agent across the organization, reducing surprises and applying standards.
  
  

Navigating the Solution Field – Key Evaluation Criteria

When choosing the right AI agent supervision solution, consider:

• Whether the platform is developer-first or built for business and product owners—business-led tools reduce reliance on engineering teams.
• Since most solutions are early in their lifecycles, check for executive team expertise and credibility.
• Progress in strategic partnerships and integrations including having MCP (model context protocol) support.
• Ability to integrate across multiple AI frameworks and monitor both internal and third-party agents.
• Availability of recommendations for improving agent performance, not just monitoring data.
• Transparent pricing, including total costs when agents move into production.

The 9 AI Agent Supervision Solution Providers Reviewed 

Wayfound has put together a list of nine potential solutions we believe are some of the strongest contenders for AI agent management and supervision. Many of the solutions on the list have been in the market for at least a year, while others are emerging just in the past few months. 

Wayfound is described first on the list, and after that, we’ve placed the other solution options in alphabetical order. All have some advantages and shortcomings highlighted.

The following vendors were reviewed: 

• Wayfound 
• Arize
• Boomi Agentstudio 
• Credo 
• Fiddler
• Galileo.ai
• LangChain’s LangSmith
• Langfuse
• ServiceNow AI Control Tower

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

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

‍The best overall for enterprise-wide AI agent supervision by business users to improve agent performance and reduce risk

• What they do: Centralized, independent platform built for business users to ensure all AI agents are continuously monitored, supervised, and optimized, across all internal and third-party AI agent interactions and actions, to help ensure adherence to business goals
• Notable: Wayfound is the first platform to offer what Gartner refers to as a ‘guardian AI Agent’ that supervises, monitors, and generates suggestions that can be copied to the Agent to quickly improve performance 
• Who it’s best for: Mid- to large-sized enterprises who are deploying or have deployed at least several AI agents, built on any framework or model, where a business (non-technical) user needs to know and act on agent performance and guardrail adherence
• Advantages:
  
  • Native integration with Salesforce Agentforce, as an official partner, and works as a complement to Salesforce Agentforce Command Center for supervising third-party agents
  • Integrates with all agent builder frameworks 
  • Offers its own Model Context Protocol (MCP) server to integrate more easily with AI agents and LLMs for supervision, self-healing, and optimization
  • Flexibly supports monitoring of any internally-built and third-party AI agents, across a variety of applications including chatbots and agentic workflows, and does this inline during AI runtime as well as offline
  • Designed for business user (non-technical) consumption, plus a low-code experience for the Dev team to hook up within minutes
  • Easy to see and understand agent knowledge gaps, action failures, user satisfaction outcomes, and compliance with guidelines/guardrails.
  • Data partnerships and integrations available to instantly detect hallucinations
  • Benchmarking of all agents (anonymized) managed by Wayfound across all clients, so that users can see how their agent performance compares to others
  • Provides sandboxing and testing capabilities before moving agent updates and guidelines to production, whether new or iterative
  • Support for multi-agent workflows
  • Recommendations to improve AI agent performance allow for fast iteration of AI agents at any phase, from design and test to deploy and refine
• Features you’ll like:
  
  • Easy-to-digest scorecards, dashboards, reports with benchmarks, and alerts that keep you focused on actions to take now vs. navigating a data deluge
  • Visual mapping of all the AI agents used across your organization
  • Extremely easy to hook up to your existing agents and environment, thanks to API and MCP integration options
  • You can freely interact with one of Wayfound’s own supervisor agents on the website and ask it questions about Agent activities and reasoning
• Shortcomings:
  
  • Currently does not offer monitoring of model costs
  • Currently does not offer token consumption tracking
• Benefits cited:
  
  • 80% decrease in AI agent supervision costs
  • Speeds any human-in-the-loop approvals by at least 30%
  • 100% of all AI agent transcripts and actions monitored and scored
• Customer feedback: Case study from customer Sauce Labs describes using Wayfound to monitor customer support agents. Feedback from that customer includes: “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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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: Their Arize AX offerings are for enterprise usage, while Phoenix is the open source tool just for LLM tracing and observability
• Who it’s best for: Open-source fan engineers and developers responsible for the entire lifecycle of building to monitoring of LLM-based AI apps and agents, particularly at larger B2C companies (although it does have B2B clients)
• Advantages:
  
  • Build and apply your own evaluators to monitor agents, enabling CI/CD where issues can be detected early
  • Co-pilot agent that helps you de-bug traces or spans, using OTEL open standards
  • Playgrounds for running prompt and dataset experiments, comparisons and replays to identify vulnerabilities and model and feature drift
  • Allows for annotations from humans to be collected
  • Monitoring that can include custom metrics and dashboards to match a customer’s preferred workflow and alerting thresholds
  • Easily dig deeper into explainability with automated root-cause analysis workflows
  • 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
• Features you’ll like:
  
  • You can search to find and capture specific data points and then drill down and explore from there
  • Heatmaps let you visually pinpoint and prioritize model performance issues
• Shortcomings:
  
  • UX is designed for developers, so does not offer a solution for business or product owners of AI apps. 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:
  
  • No specific ROI metrics cited, but they tout increased speed and reliability in deploying AI projects 
• 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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Boomi Agentstudio (https://boomi.com/platform/agentstudio/) 

• What they do: AI agent full lifecycle from design and build to orchestration and governance 
• Notable: Unclear how far along they are with the AI supervision and management capabilities in this offering, but their vision is to ensure it’s seen as an AI interoperability hub.
• Who it’s best for: Mid- to larger-sized B2C and B2B enterprises looking to accelerate AI agent adoption and rollout, but without a high development team burden.
• Advantages:
  
  • AI agent design modules allow users to build and test new agents and leverage pre-built templates that can be configured to meet your desired workflows and goals, with a process that’s helped by interacting with Boomi’s own agent to help you with this build phase
  • Design guardrails and custom attributes to define rules about how each agent can interact in their environment
  • Offers an Agent Marketplace with pre-built agents from Boomi and its partners
  • Agent Control Tower is a centralized registry for tracking, storing audit logs, and overseeing agents built with Boomi or from third parties
• Features you’ll like:
  
  • No-code interface, which means no deep AI development expertise is needed
  • Agent marketplace and templates to browse and tailor for application needs
• Shortcomings:
  
  • Agent-building and launching from templates and marketplaces is the primary focus at this point, with agent supervision, performance management, and governance areas far less developed in their suite
  • Does not monitor for and report on AI agent knowledge gaps
  • No AI agent benchmarking features available
  • No clear scorecarding of agents on performance metrics
  • Does not automatically recommend ways to improve agent performance based on specific findings it makes
  • As of August 7, 2025, its control tower can connect only to Boomi agents and Amazon Bedrock agents
• Benefits cited:
  
  • No specific ROI metrics for their Agent Control Tower supervision capabilities are listed yet on their website, but their Agentstudio launch video cites quantified benefits that can be gained by using AI agents in general.
• Customer feedback: Says they have more than 33,000 Boomi agents already deployed. Customer Jade Global testimonial is cited about using Boomi to deploy 20 agents across use cases in finance, supply chain, and customer support and helping them cut development time by 65%. Other customer logos Bits in Glass, United Techno, and Barc are listed but without usage specifics. 
• Integrations: Amazon Bedrock, and it also cites in a video tour it supports integrations with Salesforce and Netsuite. They reference use of APIs, as well as the ease of integrations by using model context protocols (MCP), but it’s unclear if Boomi has its own MCP.
• Pricing: Nothing available on their website.

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Credo (https://www.credo.ai/)  

• What they do: Governance and regulatory compliance-focused platform and related expert advisory services for enterprise-wide AI usage.
• Notable: Incorporates into the solution ways to stay compliant with new and changing government regulations of AI. Examples given on their website. 
• 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:
  
  • Centralized AI Registry for inventorying and prioritization of all AI use cases across the business (and their metadata), with project management-like views into each use case to rank them for value and risk
  • Feature called Policy Packs allows you to standardize, apply, and track adherence to business goals, GenAI guardrails, and regulations
  • Exchanges information with and collects evidence from third-party AI tools to assess their risks, via a central vendor portal
  • Generates automated 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 the performance of AI agents and models in terms of business goals and successful outcomes 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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Fiddler (https://www.fiddler.ai)

• What they do: Offers a central command center for AI 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: Developers involved in 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 AI model 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 you 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
  • Free guardrail trial offer available
• Shortcomings:
  
  • Because of the tool’s focus on developers, there is no business-friendly UI experience, making it difficult to discover, understand, and prioritize performance issues, especially across multiple agents 
  • The product UX, even though cleanly designed, can feel overwhelming with many different modules and report types 
  • Does not provide suggestions for AI agent improvement
  • Does not deliver agent benchmarking insights
  • Does not yet seem to have its own model context protocol (MCP), as of August 7, 2025
• 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, and models and identifying failures and guardrail issues.
• Notable: They bring an approach 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 20 pre-built AI agent evaluators to handle specific analytics and metrics reporting, such as RAG metrics, Safety metrics, Security metrics, and more.
  • Allows you to build custom evaluator agents to meet your needs
  • Observability is done in real-time in production, while capturing detailed traces 
  • Includes latency and costs, clearly transparent
  • 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)
  • Graph views that 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
  • Does not bake-in 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, only that some failure or poor outcome resulted
  • Does not yet seem to have its own model context protocol (MCP), as of August 7, 2025
  • Developer-focused, no experience for business users
• 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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LangChain’s LangSmith (https://www.langchain.com/langsmith)

• What they do: Unified observability platform for development teams to 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, although it does support other frameworks as well. 
• Who it’s best for: AI app development teams especially those using the LangChain framework
• Advantages:
  
  • Save production traces, which include 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:
  
  • Does not provide automatic scorecarding and benchmarking of agent performance
  • Does not automatically identify knowledge gaps and make improvement recommendations
  • 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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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 and liked open source observability and debugging tool generally found more so 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
  • 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
• 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 
  • Solution’s AI does not really recommend or suggest where to prioritize or improve, just presents the data for you to interpret
  • 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 7 million SDK installs per month and 6 million Dokker pulls as of August 5, 2025. Over 24,000 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: Can get started on their Hobby (proof of concept) plan 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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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 any and all AI initiatives
• Notable: Fairly new offering and light on details, but does seem to cover the basics of agent supervision needs
• 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
  • 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
• Shortcomings:
  
  • Early offering, just launched in May 2025. Very little information, details, and product glimpses of Control Tower available as of August 8, 2025.
  • Does not appear to address automatic surfacing and alerting of agent knowledge gaps 
  • Does not appear to offer agent performance benchmarking
  • Does not appear to provide automatic agent-driven recommendations on how to improve the agents its supervising and the context of why those recommendations are being made 
  • Unclear who the primary user will be, i.e. whether a developer-first experience or business-side managers
• Benefits cited:
  
  • No specifics 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 
• Integrations: No specific details provided
• Pricing: No pricing information available on their website

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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 list of solutions to consider for AI agent supervision and management success 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 miscellaneous developer websites, 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.

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About the Author: Wayfound

Launched in April 2024, Wayfound is the world’s first no-code independent AI Agent Management and Supervision platform, ensuring business users can monitor, manage, and actively supervise AI agents for compliance to guardrails, user sentiment, knowledge gaps, effectiveness of tool calls, and agent-to-agent interaction. Customers are able to deploy AI agents 75% faster with Wayfound. 

Ask more about Wayfound by talking to its agent at https://www.wayfound.ai/chat or by seeing the full solution in action https://www.wayfound.ai/request-a-demo 

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