April 2026 Summaries

The text compares four API integration platforms—Nango, Arcade.dev, Composio, and Merge Agent Handler—focusing on their suitability for AI coding agents like Claude Code, Cursor, and Codex. Nango is highlighted for its agentic capabilities, supporting over 700 APIs and offering a comprehensive AI builder skill that allows coding agents to research, write, test, and deploy integrations directly within a development environment. It features robust runtime capabilities, including an MCP server, data syncs, webhooks, and OpenTelemetry observability. Arcade.dev is noted for its MCP-first runtime but lacks a coding-agent skill and broader integration patterns like data syncs. Composio offers a large catalog of pre-built tools with managed authentication but is closed-source and lacks custom tool deployment. Merge Agent Handler provides pre-built tool calls and a catalog of connectors but does not support custom tool calls, data syncs, or webhooks. The text emphasizes that while all platforms provide an MCP server, Nango uniquely supports end-to-end integration development within the same platform, making it ideal for teams needing both build and runtime integration capabilities.

In the evolving landscape of API integration platforms for AI coding agents, four major platforms—Nango, Arcade.dev, Composio, and Merge Agent Handler—are evaluated based on their support for AI workflows, Model Context Protocol (MCP) compatibility, and runtime capabilities. Nango stands out as an open-source platform offering robust support for coding agents like Claude Code and Codex, enabling end-to-end integration development with a strong focus on real-time testing, observability, and extensive API catalog. Arcade.dev is tailored for MCP-first runtime environments, focusing on tool calling but lacking support for broader integration patterns or coding-agent development workflows. Composio provides a closed-source tool gateway with a significant pre-built tool catalog and managed authentication, suitable for internal productivity but limited in customization and data syncs. Merge Agent Handler offers pre-built connectors and tool packs for specific SaaS actions, but does not support custom tool calls or broader integration capabilities. Overall, Nango is highlighted as the most comprehensive platform for building and running API integrations with AI coding agents due to its open-source nature, extensive API support, and real-time operational features.

An agentic API integrations platform is an advanced system where AI coding agents autonomously build API integrations, which are then utilized by AI agents at runtime to perform various tasks, such as fetching data or sending messages. This platform marks a shift from earlier models like embedded iPaaS and unified APIs, enabling just-in-time integrations that are generated on demand rather than pre-built for every use case. Notable platforms in this category include Nango, which supports over 700 APIs and allows seamless integration using AI agents; Arcade, which emphasizes tool calling with a focus on MCP runtimes; and Composio, which offers a large catalog of pre-built tools but lacks customization capabilities. These platforms leverage AI to efficiently handle the increasing demand for integrations, allowing teams to maintain control over integration processes while minimizing human involvement.

An agentic API integrations platform represents a new category of technology that allows AI coding agents to autonomously build and manage API integrations, while AI agents consume these integrations in real-time, thereby reducing the need for human intervention. This platform offers a comprehensive stack that includes integrations-as-code, a secure runtime, and managed authentication for external APIs with data synchronization. Unlike its predecessors, such as embedded iPaaS and unified APIs, agentic platforms cater to the growing demand for just-in-time integrations where integrations are generated on demand. The text evaluates three platforms—Nango, Arcade, and Composio—highlighting Nango's open-source capabilities and comprehensive support for building and consuming integrations, as opposed to Arcade's limited focus on MCP-first runtime and Composio's large catalog of pre-built tools without customization options. This shift towards agentic platforms is driven by advancements in AI coding agents like Claude Code, Cursor, Codex, and Gemini CLI, which can read API documentation and autonomously build integrations, thus enabling faster and more scalable integration processes.

Nango has announced several milestones and new features aimed at enhancing its API integration capabilities and supporting AI use cases. With a recent funding boost of $7.5 million led by Gradient, Nango is focused on developing integrations through AI and expanding its offerings, including the introduction of a new API integrations builder skill. The company has also launched role-based access control to bolster security for enterprise teams and migrated from Temporal to a Postgres-based task orchestrator to improve task management. Additionally, Nango's initiatives during its AI Launch Week included offering real-time syncing capabilities, improved observability through OpenTelemetry, and a custom integration service, all designed to make integration processes more efficient and reliable. The efforts are also complemented by insights gained from consulting services and the development of a robust developer infrastructure team, as well as lessons learned from building over 200 API integrations.

AI coding agents such as Claude Code, Cursor, and Codex have significantly reduced the time required to build API integrations, turning what used to take weeks into just a few hours. These agents excel in researching documentation, writing code, and iterating on failures with minimal manual intervention, but require more than just coding abilities to be effective. Platforms like Nango provide the necessary infrastructure, including managed authentication, testing against real APIs, integration-specific patterns, deployment infrastructure, and observability, which are crucial for developing production-ready API integrations. With the right platform, agents can generate, test, and deploy API integrations efficiently by leveraging reusable skills that encode domain-specific expertise. This allows for seamless integration with external APIs such as Salesforce, Slack, and HubSpot, by handling complex tasks like OAuth implementation, data syncing, and error management. As AI models and platforms continue to evolve, the need for a robust, code-first integration platform remains essential to harness the full potential of AI coding agents in building scalable, reliable API integrations.

AI coding agents, such as Claude Code, Cursor, Codex, and others, are revolutionizing the development of API integrations by significantly reducing the time required from weeks to mere hours. These agents excel at tasks like researching API documentation, generating boilerplate code, and iterating on failures, but they require more than just coding skills to create production-ready integrations. The key lies in pairing these agents with integration platforms that offer managed authentication, real API testing, deployment infrastructure, and observability, which help bridge the gap between code generation and practical application. Platforms like Nango provide AI builder skills that equip coding agents with the necessary context to handle complex integration requirements, such as OAuth management, pagination, and webhook handling, ensuring the agents can deliver robust, end-to-end API integrations. While the specific AI agent used is less critical than the supporting infrastructure, having the right skills and tools in place is essential for achieving seamless and efficient integration processes.

Nango's experience in building and maintaining API integration support for over 700 providers has led them to identify strategies for developers who need to ship integrations without access to clean API sandboxes or developer accounts. APIs generally fall into two categories: developer-friendly, which offer free sandboxes or accounts, and enterprise-gated, which restrict access to paying customers and certified partners. Strategies for overcoming access barriers include obtaining free developer accounts, negotiating directly with providers, partnering with consultants or existing customers, building from documentation, and testing beta versions with initial customers. Nango supports these efforts with its open-source integration platform, providing pre-built authentication, durable syncs, and reusable integration templates to reduce infrastructure risks. The platform also leverages AI coding agents to accelerate integration development by generating production-ready code and handling complex infrastructure tasks, allowing developers to focus on business logic rather than API-specific challenges.

Nango offers insights into building API integrations, particularly when developers cannot access a test account from the provider. The article categorizes APIs into developer-friendly, which offer free sandboxes or developer accounts, and enterprise-gated, which restrict access to paying customers or certified partners. Nango suggests strategies for dealing with each category, starting with obtaining a free developer account or negotiating directly with providers for temporary access. If these avenues fail, developers can partner with existing account holders or rely on well-documented APIs, using tools like API mocking and AI coding agents to simulate and build integrations. Furthermore, Nango provides infrastructure support with pre-built authentication, durable syncs, and reusable templates, reducing the risk and complexity of integration development. The platform emphasizes the importance of collaboration with early customers to test and refine integrations in live environments, ultimately focusing on business logic rather than infrastructure challenges.

Integrations, traditionally treated as complex engineering projects, are being revolutionized by AI coding agents capable of autonomously reading API documentation and building integrations. This shift has led to the development of just-in-time (JIT) integrations, which are created in response to specific customer needs, allowing for hyper-customized solutions at a significantly reduced cost. JIT integrations transform the traditional model by focusing on building a system or "factory" that generates integrations on demand rather than crafting each one individually. This approach reduces the marginal cost of new integrations to near zero and allows customers to self-serve many integration needs without waiting for product team prioritization. As a result, integration quality improves through continuous monitoring and real-time improvements by AI agents. However, the challenge lies in constructing a reliable factory capable of generating these JIT integrations, which requires a controlled environment, secure runtime, and robust observability to ensure seamless operation and maintenance. The evolution towards JIT integrations is ongoing, with some companies already implementing them, and the expectation is for rapid maturation as technology and methods advance.

AI coding agents are transforming the landscape of software integrations by making the process of building and maintaining them significantly easier and faster, shifting from manual efforts to just-in-time (JIT) integrations. These agents can autonomously read API documentation, iterate, and produce integrations on demand, allowing companies to generate hyper-custom integrations tailored to individual customer needs, reducing the marginal cost to nearly zero. This shift enables customers to self-serve many integrations without waiting for product team prioritization, as the system can build integrations based on specific customer requests. JIT integrations improve quality by continuously monitoring and adjusting to API changes, offering a more dynamic and real-time maintenance approach compared to traditional methods. The focus now moves to developing the factory system that can reliably produce these integrations, requiring controlled environments, secure and scalable runtimes, and robust observability tools. While many teams are currently utilizing AI-assisted integrations, the transition to fully JIT integrations is already underway in some products, promising further advancements as AI models and systems continue to develop.

Integrating Google Calendar with real-time event updates requires a complex setup involving push notifications and incremental sync to ensure data consistency and responsiveness. Push notifications provide instant alerts about calendar changes but lack detailed event data, necessitating the use of incremental sync to fetch updated event information using sync tokens. The integration process is complicated by challenges such as managing OAuth token lifecycles, renewing webhook channels, verifying domains, handling sync token errors, and adhering to API rate limits. Nango, a code-first integration platform, simplifies this process by managing the underlying infrastructure, including OAuth handling, webhook routing, and sync scheduling. It provides tools like AI Function Builder to automate code generation for these integrations, allowing developers to focus on implementing business logic while Nango ensures scalability and reliability across its supported APIs.

Integrating real-time Google Calendar sync into a product requires a combination of push notifications and incremental sync to ensure data consistency and quick updates, as meeting changes occur. Push notifications provide near-instant alerts whenever a change is made to a calendar, but they don't include the event data, which requires a subsequent incremental sync to fetch the actual changes. This dual approach ensures timely updates while overcoming the unreliability of push notifications alone due to potential missed signals. Implementing this system from scratch is complex, involving challenges such as managing OAuth tokens, renewing webhook channels, verifying domains, and handling sync token management, all while staying within API rate limits. The code-first integration platform Nango simplifies this process by offering out-of-the-box solutions for OAuth, durable sync infrastructure, and webhook management, allowing developers to implement a complete real-time sync system efficiently. Nango also provides tools like the AI Function Builder to generate integration code, enabling rapid development of Google Calendar event syncs that automatically handle channel renewals, process webhooks, and update backend systems.

Custom objects are user-defined data models that extend a CRM or ERP beyond its standard schema, requiring API integrations to adapt to each unique customer schema. Integrating these custom objects involves three main steps: discovering the customer's schema via metadata APIs, allowing customers to configure field mappings through a UI or code, and reliably syncing data at scale. Different mapping approaches, such as declarative mapping, per-field code transformers, and full code-level transformation, cater to varying complexities of data transformation needs. The process also involves managing challenges like schema drift, permission gaps, rate limits, and data validation, with solutions like incremental syncs, checkpointing, and durable execution. Tools like Nango offer a unified interface for managing these integrations, providing structured logs, connection metadata storage, and support for 700+ APIs, thereby easing the implementation and maintenance of custom object integrations while focusing on mapping logic.

Custom objects are user-defined data models that extend CRM or ERP systems beyond their standard schema, necessitating adaptable API integrations to handle diverse customer data. Integrations involve discovering customer schemas, allowing customers to configure data mappings, and ensuring scalable data syncing. Discovering schemas requires listing and describing objects via metadata APIs, caching schema data, and managing changes through polling or webhooks. Customers configure field mappings using declarative, per-field code transformers, or full code-level transformation for complex cases. Syncing data at scale involves handling large datasets, implementing incremental syncs, respecting rate limits, and ensuring durable execution with checkpointing. Integrations also include writing back data, requiring conflict resolution, validation, and handling partial failures. Nango is a platform that facilitates these processes by providing a unified interface for managing auth, data syncs, and schema management across numerous APIs, allowing for quick implementation and scalability while managing ongoing complexities like schema drift and permission changes.

Enterprise customers often heavily customize their CRM and ERP systems, creating challenges for products that integrate with these platforms. These customizations typically involve adding custom objects and fields that standard APIs do not anticipate, necessitating advanced solutions for schema discovery, customer-configurable field mappings, and efficient data synchronization. Platforms like Nango, Merge, Prismatic, and Workato offer varying capabilities to address these challenges, with Nango providing comprehensive API access and code-first development for deep custom object support, while Merge offers a unified API for select CRMs with limited customization. Prismatic and Workato cater to teams needing visual builders and dynamic field mapping features but have limitations regarding custom object support and complex transformations. The effectiveness of each platform depends on its ability to discover existing schemas, allow customers to map their data, and sync data efficiently, with Nango standing out for its full API access and AI coding agent support, enabling robust per-customer customization and reliable data syncing.

Enterprise customers often heavily customize their CRM and ERP systems, which presents unique challenges when integrating with these platforms. These customizations typically involve custom objects and fields that standard API integration platforms are not designed to handle efficiently. To address these challenges, integration platforms like Nango, Merge, Prismatic, and Workato offer various approaches to schema discovery, field mapping configuration, and data syncing. Nango provides full API access and a code-first approach, enabling detailed per-customer customization and robust sync infrastructure. Merge offers a pre-built unified API with limited custom object support for select CRMs, while Prismatic uses a visual workflow builder enhanced by AI-powered mapping suggestions. Workato focuses on non-technical users with a visual builder but requires workarounds for custom object support. The integration complexity increases with custom objects, requiring platforms to efficiently handle schema discovery, customer-configurable field mappings, and large data syncs, with AI coding agents playing a significant role in reducing engineering costs for per-customer customizations.