A structured framework for objectively evaluating AI agent platforms across eight critical dimensions—from no-code building to enterprise security—enabling confident, criteria-driven decisions for your customer experience technology investments.
A drag-and-drop interface that allows non-technical users to create and modify AI agent workflows or teams of Agents without writing code.
Look for visual workflow builders, flowchart-style interfaces, or GUI-based agent configuration tools. Must be accessible to business users, not just developers.
Comprehensive software development kits that provide pre-built functions, classes, and utilities for building AI agents programmatically. This would not include SDKs for just talking to or using agents or AI functionality, it must be an SDK, typically TypeScript or Python, that fully defines how an agent works and what it does in a declarative way.
Must include documentation, code examples, and framework support (like React, FastAPI, etc.). Look for official SDKs, not just API wrappers.
Changes made in the visual builder automatically update the underlying code, and code changes reflect in the UI interface.
Must demonstrate bidirectional synchronization. Changes in either interface should be reflected in the other without data loss.
Support for Model Context Protocol servers, enabling standardized tool and data source integrations.
Must explicitly support MCP protocol or demonstrate compatibility with MCP servers. Look for MCP-specific documentation or integrations.
Systems that coordinate multiple specialized agents using graph-based workflows or decision trees.
Must support multiple agents working together with defined relationships and handoff logic. Look for visual workflow representations or agent collaboration features.
Support for both delegating tasks to sub-agents while maintaining control, and fully handing off conversations to specialized agents.
Must demonstrate both patterns - delegation (supervisor remains involved) and handoff (full transfer of control). Should show clear examples of each.
Direct communication channels between agents without human intervention, enabling collaborative problem-solving.
Must show agents communicating directly with each other, sharing context, or collaborating on tasks. Should be more than just sequential workflows.
Individual authentication and authorization systems for each agent, allowing different access levels and API keys.
Must allow different agents to have different credentials, API keys, or access permissions. Should support credential isolation and management.
Detailed logging and tracing of agent actions with visual interfaces and industry-standard telemetry.
Must provide visual trace interfaces showing agent decision-making and support OpenTelemetry standards for observability.
Systems that automatically discover, crawl, and index publicly available information sources.
Look for web crawling capabilities, RSS feed ingestion, public API integrations, or automated content discovery. Must be ongoing, not one-time imports.
Direct integrations that automatically sync content from private knowledge management systems.
Must have native integrations (not just manual uploads) with popular enterprise tools. Should handle permissions and access controls.
Advanced retrieval-augmented generation with intelligent chunking, embedding optimization, and relevance scoring.
Look for advanced RAG features like semantic chunking, hybrid search, relevance tuning, or retrieval optimization. Must be more sophisticated than basic vector search.
Ability to query live data sources during conversations, not just pre-indexed static content.
Must demonstrate live API calls, database queries, or web scraping during agent interactions. Should handle authentication and rate limiting.
Automated systems that refresh and update the agent's knowledge (from internal & external sources like website & docs) without manual intervention.
Look for scheduled updates, webhook-based updates, or real-time syncing with data sources. Must handle changes automatically.
AI agents are callable inside Claude, ChatGPT, and Cursor via each platform's native tool/action interface and can execute at least workflows end-to-end.
Evidence of a working, documented integration: official listing/docs + runnable setup + a successful end-to-end workflow in the target surface (no "theoretical support").
Native bot integrations that let agents run tasks, respond, and interact within team chats (not just webhooks).
Must include native bot apps with rich, interactive features (slash commands, buttons, threads). One or more workflows must run fully inside Slack/Discord with proper auth and error handling.
Direct integrations with major CRM and customer service platforms for seamless workflow integration.
Must provide native integrations with ticket creation, customer data access, or workflow automation. Should be more than just API connections.
Dedicated conversational AI Agent for customer support that knows everything about the product and company that can search, cite, and handoff questions to other support questions when needed.
Must be able to be based on indexed data in a company's internal and external docs. Must be fully configurable for control and customization.
Responses that include specific references to source documents with clickable links or clear attribution.
Must provide traceable sources for generated content. Look for clickable links, document references, or clear source attribution in responses.
Safety mechanisms that prevent inappropriate responses and confidence thresholds that trigger human escalation.
Must include content filtering, response confidence scoring, and automatic escalation when confidence is low. Should show safety mechanisms in action.
Advanced search capabilities that understand context and meaning, not just keyword matching.
Must demonstrate semantic search capabilities across enterprise data sources with relevance ranking and context understanding.
Built-in capabilities for automated generation of documentation or marketing copy, documentation based on product gaps and feature gaps discovered by AI Agents.
Must include AI content generation features specifically designed for creating new content automatically based on feature gaps and knowledge base gaps.
Analytics that identify what information is missing from the knowledge base.
Must provide insights into unanswered questions, missing information. Should include actionable recommendations.
Analytics that identify what information is missing from the knowledge base or what features users are requesting.
Must provide insights into unanswered questions, missing information, or feature requests. Should include actionable recommendations.
Pre-built, customizable JS user interface components that can be embedded in AI Agents chats.
Must provide actual JavaScript components, not just embeddable widgets specifically for AI Agent UI Chats. Should include customization options and documentation.
Pre-built, customizable React user interface components that can be embedded in AI Agents chats.
Must provide actual React components, not just embeddable widgets specifically for AI Agent UI Chats. Should include customization options and documentation.
UI elements that allow users to interact beyond simple text chat, including forms, buttons, cards, and other rich interactions.
Must support interactive elements within the chat interface. Look for form handling, button actions, card-based responses, and rich media support.
Compatibility with Vercel's AI SDK formats and streaming protocols for web applications.
Must support Vercel AI SDK formats, streaming responses, or demonstrate integration with Vercel ecosystem. Look for specific SDK compatibility.
Single Sign-On integration with automated user provisioning and deprovisioning through SCIM protocol.
Must support major SSO providers (Okta, Azure AD, etc.) and demonstrate SCIM-based user lifecycle management.
Granular permission systems that control what different user roles can access and modify.
Must provide role definition capabilities, permission matrices, and demonstrate granular access controls across platform features.
Comprehensive logging of all user actions, system changes, and agent interactions for compliance and security.
Must provide detailed audit trails with timestamps, user attribution, and action details. Should support log export and retention policies.
Flexible deployment options including both managed cloud services and on-premises installations.
Must offer both deployment models with feature parity. Self-hosted options should include installation guides and support.
Automated systems that detect and remove personally identifiable information from conversations and data.
Must demonstrate active PII detection and removal, not just data masking. Should include multiple PII types and configurable policies.
Contractual commitments to system availability and support response times with measurable guarantees.
Must provide specific uptime percentages and support response time commitments. Look for SLA documentation and performance reporting.
Successfully completed SOC 2 Type II audit demonstrating operational effectiveness of security controls over time.
Must have valid SOC 2 Type II certification, not just SOC 2 Type I. Look for recent audit reports or compliance badges.
Platform configurations and features that enable compliance with data privacy and healthcare regulations.
Must provide specific compliance features, not just general security. Look for data processing agreements, privacy controls, and compliance documentation.
Find a time with our Agent Solutions team to get an overview of Inkeep Enterprise and demo of Inkeep Agents for your use case.
