Five ways to build product integrations in 2026

TL;DR

There are five practical ways to build product API integrations (also called in-app integrations or native integrations) in 2026:

• Build from scratch: Maximum flexibility and direct API access, but you and your team own every layer of the stack: auth, retries, webhooks, scaling, and ongoing maintenance.
• Low-code embedded iPaaS (2010s era): A visual workflow builder embedded in your product. Useful for demos and one-off internal workflows, but the pre-built connectors offer limited support for custom objects and custom fields, and the visual builder is not a fit for AI coding agents.Examples: Tray.io, Paragon, Workato Embedded.
• Pre-built unified APIs (early 2020s era): One normalized API across a category of providers, so you integrate once and get many providers in that category. The trade-off is that the unified schema is limited to common fields, the catalog is fixed, and custom objects and custom fields are not supported well.
  Examples: Merge, Codat, Apideck.
• Code-first integration platforms (transition era): Pre-built auth, runtime, and observability that lets your engineers write each integration as code, with full access to the external API. Most infrastructure work is handled by the platform, while integrations stay in your repo and in your CI/CD. Nango supports this, including the emerging Agentic API integrations pattern.
• Agentic API integrations platforms (2026+): The platform is still code-first, but developers use AI coding agents to build and deploy integrations on the platform, the AI agents inside your product consume them at runtime, and integrations can be generated just-in-time for a specific customer. The newest category, and where Nango sits today.

Nango is an open-source agentic API integrations platform supporting 700+ APIs. Developers pair their favorite coding agent (Claude Code, Cursor, Codex, Gemini CLI, OpenCode, and more) with the Nango AI builder skill to build and deploy integrations on the platform. The AI agents inside your product consume those same integrations through an MCP server, typed tool calls, webhooks, and data syncs.

Why product integrations changed in 2026

Building integrations on third-party APIs has always been challenging. Getting to a production-ready integration takes weeks once token refresh, rate limits, schema mappings, retries, scheduling, and observability are considered. For most of the last decade, teams treated integrations as roadmap items and shipped only three to five a quarter. AI coding agents have started to change that math.

Two shifts have happened since October 2025:

1. Coding agents can build integrations autonomously. Claude Code, Cursor, Codex, and Gemini CLI can read API docs, write integration code, and iterate on failing requests until tests pass. We built 200+ integrations in 15 minutes using OpenCode and the Nango builder skill.
2. AI agents in SaaS products need API access at runtime. Agents read context from external systems and take actions in them: fetch a customer record from the CRM, send a Slack message, draft an email in Gmail, or update a ticket in Linear.

The five options below cover where teams actually build product integrations today, ordered roughly from “you do everything yourself” to “the platform and the coding agent do most of the work.”

Factors to consider before you pick

No two integrations are the same. The right approach depends on your product and the customers using it. Five questions to help decide:

• Direction: Read from the API, write to it, or two-way sync?
• Depth: One or two standard objects, or a large surface of the external system, including custom objects and fields?
• Source complexity: A calendar API is simple. CRMs, ERPs, and HRIS systems are not. Some have undocumented behaviors and instance-specific quirks.
• Customer footprint: One or two enterprise accounts, or every customer in the product?
• Volume: Pushing a few form submissions a day, or syncing millions of records continuously?
• Catalog size: Two or three deep integrations, or wide coverage across dozens of providers?

If you need wide coverage with deep, customer-specific integrations and you are also building AI agent features, the answer is almost always Option 5.

The five ways to build product integrations

Option 1: Build from scratch

The first option is to write the integration directly in your product. An HTTP client, a background job runner, a queue, an OAuth library or your own implementation, and all the logic and error handling on top. AI coding agents can now generate most of this boilerplate, which is a real shift from a few years ago.

Best for

Systems without proper APIs (SFTP, email parsing, scraping, on-prem), legacy or in-house systems where you have full ownership of both ends, and one-off integrations where the cost of a platform is not justified and the scope is unlikely to grow.

Pros

• Full API access: There is no abstraction in the way, so you can hit any endpoint and use any field, including custom objects and custom fields.
• AI coding agents accelerate the code: A coding agent can read the provider’s docs and produce a working request in minutes, so the build step is no longer the bottleneck it used to be.

Cons

• You build and maintain all the infrastructure: Pagination, token refresh and refresh concurrency, rate limit handling, schema mapping, scheduling, retries, dead-letter queues, observability, and tenant isolation all land on your team. Teams consistently underestimate how much of this work they actually need to do.
• Scaling is on you: The first integration usually looks fine. The tenth one, syncing millions of records per customer, is where the architecture has to be right by design rather than improvised under pressure.
• No AI agent runtime: If your product has AI agents that need to call these integrations as tool calls or MCP endpoints, you are also on the hook for building that consumer layer yourself.

Option 2: Low-code or no-code embedded iPaaS

Embedded iPaaS platforms came out of the 2010s low-code automation wave. They expose a visual workflow builder inside your product, with pre-built connectors and pre-built actions. You drag actions into a workflow, optionally drop in a code step, and the platform runs the result for your customers.

Example providers: Tray.io, Paragon, Workato Embedded, Prismatic.

Best for

One-off integrations for specific enterprise customers, built and maintained by a customer success team, and simple “push a record into the customer’s CRM” use cases where the pre-built actions are enough.

Pros

• Pre-built connectors and actions: Quick to demo, quick to ship a basic integration in a category the platform already covers.
• Non-technical builders: Customer success or implementation teams can build one-off workflows without engineering involvement.

Cons

• Fixed catalog: Pre-built connectors and actions cover a small slice of each external API. Anything missing has to wait for the vendor or be worked around.
• Scale ceiling: These platforms were designed for low-volume internal automation. Enterprise customers syncing 1000s of records a minute often hit hard execution limits, timeouts, or rate caps.
• No coding agent fit: Visual builders are not where Claude Code, Cursor, or Codex are productive. The build pipeline assumes a human in a UI.
• Vendor lock-in on auth: Customers often authorize the platform, not your app. Migrating later is painful.

For a deeper view of the category, see why embedded iPaaS are a bad idea.

Option 3: Pre-built unified APIs

Unified APIs popularized in the early 2020s the idea that you could integrate one API and get access to many providers in the same category, with one API for HRIS, one for CRMs, one for accounting, and so on. Internally, the platform normalizes the underlying providers into a common data schema, so your team works against a single shape regardless of which provider they’re interacting with.

Example providers: Merge, Codat, Apideck.

Best for

Categories where the unified schema covers your customers’ real use cases (HRIS employee data is the strongest fit, basic ATS candidate data also works), and teams that need wide but basic coverage of one category fast.

Pros

• Wide category coverage with one integration: Integrate once, support dozens of providers in a category.
• One data model to learn: The team works against the unified schema, not the quirks of each provider.
• Pre-built auth UI components: Most ship a drop-in widget for connecting accounts.

Cons

• Lowest-common-denominator schema: The unified API exposes only the fields shared by every provider in the category. For example, Salesforce and HubSpot contacts only share about five fields out of the box.
• Custom objects and fields are second-class: Real B2B customers add custom objects (Implementation_Tracker__c) and fields (Salesforce_Region__c) all the time. Pre-built unified APIs cannot represent them cleanly. See why B2B SaaS outgrow pre-built unified APIs.
• Fixed catalog: Only specific provider categories are covered. If you need an API that does not fit, you build it yourself outside the unified API.
• Limited extensibility: Adding a missing field, object, or action requires the vendor to ship it. You will have to wait.
• Limited per-customer configuration: Field mappings, filters, and tenant-specific behavior are usually not supported, or live behind enterprise plans.
• No coding agent fit, no AI agent runtime: The platform is built for a backend engineer querying a fixed catalog, not for coding agents producing integrations or AI agents consuming them.

One related approach is building your own custom unified API on top of a code-first platform, which keeps the unified shape without inheriting the closed schema.

Option 4: Code-first integration platforms

In a code-first integration platform, the platform handles auth, runtime, retries, webhooks, and observability, while your team writes each integration as code in your own repo.

In practice, what your team writes for each integration is:

• Auth setup: A short config that registers the provider’s auth (OAuth, API key, JWT, basic auth), plus a drop-in connect UI from the platform that triggers the user-facing auth flow.
• Actions: A typed function like create-salesforce-contact or send-slack-message that your backend or your AI agent invokes deterministically.
• Syncs: A function that pulls records from the provider with checkpoints, pagination, and delete detection. The platform handles incremental refresh and retries on top of your code.
• Triggers: Webhook subscriptions or polling triggers that route provider events to handlers you write.

Best for

Teams that want full API access without owning the auth, runtime, and observability layers themselves, and that prefer to keep each integration as code in their own repo.

Pros

• No coverage limit: Anything the external API exposes, you can use, including custom objects, custom fields, and undocumented endpoints.
• Lives in your codebase: Integrations are TypeScript files in your repo, deployed through CI/CD, version-controlled and reviewable.
• Scales out of the box: Durable syncs, webhook ingestion, and per-tenant resource limits are handled by the platform.

Cons

• You still write each integration: A human engineer is still authoring the integration code per provider, unless the platform also offers the agentic build loop from Option 5.

Option 5: Agentic API integrations platforms

This is the newest category, and the one designed for how teams actually build product integrations in 2026.

An agentic API integrations platform serves two first-class users at once:

On the build side, your developers use AI coding agents to author integrations on the platform, with a skill or harness that encodes the integration patterns and a test loop that runs against real APIs.

On the run side, the AI agents in your product consume the same integrations at runtime via tool calls, an MCP server, polling triggers, and data syncs.

Integrations live as code in your repo, but most of that code is written and tested by a coding agent, reviewed by an engineer, and deployed through normal CI/CD. Where a customer asks for an integration the team has not pre-built, the same platform can generate just-in-time integrations.

This category is the only option that scales with the AI economics of 2026, because the more the coding agent does, the less human involvement each new integration requires.

Three things an agentic platform gives you

1. A build loop for coding agents: A skill or SDK that encodes integration patterns (auth, pagination, delete detection, checkpoints, error handling), a test command that runs against a real connection, and a deployment path through CI/CD.
2. A secure, scalable runtime: Managed auth across providers, durable syncs, webhook ingestion, rate-limit handling per API, and tenant isolation so one customer’s integration cannot affect another’s.
3. An AI agent consumer surface: Typed tool calls, an MCP server, polling triggers, and data syncs, so the AI agent in your product can act on external systems and pull context for RAG without a separate integration layer.

Nango is the open-source agentic API integrations platform built around exactly this model.

The foundation: managed auth across 700+ APIs (OAuth, API keys, JWT, basic auth, MCP Auth), durable syncs, per-customer configuration, a tenant-isolated runtime with deep observability, and a drop-in white-label connect UI. Tool calls add less than 100ms of overhead.

The build side: an AI builder skill that works with 18+ coding agents (Claude Code, Cursor, Codex, Gemini CLI, OpenCode, and more), and a nango dryrun command that runs generated code against a real connection so the agent iterates on real responses instead of hallucinating endpoints.

The run side: a built-in MCP server with typed tool calls, data syncs for RAG, webhook processing, and polling triggers, all from the same platform. Coding agents can also generate just-in-time integrations on demand. Nango is open-source, SOC 2 Type II, GDPR, and HIPAA compliant, with Enterprise self-hosting available.

# Install the Nango skills for your coding agent
npx skills add NangoHQ/skills -s building-nango-functions-locally

Then describe the integration you want in plain English. For example:

/building-nango-functions-locally Build a Nango action that 
creates an event in the user's calendar using the Google Calendar API.
Event details:
1. title: nango-test
2. date: today

The skill handles the rest. The agent researches the API, writes the integration code, runs nango dryrun against a real connection, and iterates on errors until tests pass.

The API integration is now ready to use in your application. You can also run the custom actions and syncs in the Nango playground.

Best for

Teams building production AI agents that need to call external APIs reliably and want a single platform for both the build and the run side, products where customers ask for hundreds of integrations and engineering cannot pre-build every use case one at a time, and teams already using AI coding agents who want to compound that velocity into integrations.

Pros

• Developers ship faster with coding agents: With the right skill, an engineer pairing with Claude Code, Cursor, Codex, or Gemini CLI can produce a working integration in under an hour, including authentication and error handling. The engineer reviews and ships each step, so the team stays in control of every layer.
• Same code at build and runtime: What the agent writes today runs unmodified on a hardened, tenant-isolated runtime tomorrow.
• AI agent runtime included: MCP server, tool calls, webhooks, and syncs are part of the platform, not a separate stack.
• Just-in-time integrations: Integrations can be generated on demand for a specific customer, with their schema, their fields, and their workflows.
• Customers can self-serve: Customers can describe what they need, and the system builds it. Not for every case yet, but already a fit for a growing set of integrations.
• Quality compounds over time: A coding agent can monitor logs, exception traces, and API responses continuously and improve the integration. Closer to 24/7 maintenance than a human checking in periodically.

Cons

• Few platforms cover the full pattern today: Most vendors ship only one or two of the three layers (build loop, runtime, consumer surface). Nango is the only platform we are aware of that covers all three end-to-end.
• Emerging category: Quality varies across vendors. Treat the evaluation checklist as the bar.

For a deeper view, see the best agentic API integrations platform in 2026.

Comparison table

FAQ

What’s the best way to build product integrations in 2026?

For most teams shipping product integrations alongside an AI surface, an agentic API integrations platform (Option 5) is the right default. Coding agents do the build work, the runtime is included, and integrations stay as code in your repo.

How do AI coding agents help build product integrations?

AI coding agents collapse the build step from weeks to hours. The catch is that integrations need more than code: managed auth, retries, durable execution, webhook ingestion, rate-limit handling, observability. A coding agent without that infrastructure produces code that compiles and fails in production. The platform that pairs an AI builder skill with this infrastructure is what makes the difference.

What is a just-in-time integration?

A just-in-time integration is generated on demand when a customer needs it, rather than pre-built by the platform team. The most common implementation today is a prompt box where a customer describes the integration they want and an agent builds it against their account. The marginal cost of one more integration drops close to zero, which makes it realistic to cover hundreds of providers instead of just the top 20.

Where does a unified API still make sense?

Unified APIs work best where the normalized schema actually represents your customers’ data. A good example is employee data from HRIS platforms, where the core fields are consistent enough across providers that one schema fits. Basic candidate data from ATS platforms is another reasonable fit, though things like job-specific application questions already start to show the practical limits. Categories like CRM and accounting tend not to work well in practice, since custom objects, custom fields, and per-customer logic break the schema, and most of those teams end up moving to Option 4 or Option 5.

Is a code-first integration platform the same as an agentic platform?

No. A code-first platform handles the runtime side: auth, syncs, webhooks, observability. An agentic platform also ships an AI builder skill so coding agents can produce integrations end to end, plus an AI agent runtime (MCP server, typed tool calls) for consumption. Option 5 is the superset of Option 4.

Related reading

• The emergence of just-in-time integrations
• Best agentic API integrations platform in 2026
• Using AI coding agents for building API integrations in 2026
• Should you build or buy product integrations?
• The hidden costs of building product integrations in-house