How We Built an AI Agent to Create Integrations at Scale

What happens when you stop treating integration development as artisanal craftsmanship

Building integrations used to be one of our biggest bottlenecks.

At Clutch, integrations are the foundation of everything we do. Our platform delivers visibility into non-human identity security by connecting to the systems where machine identities actually live: cloud providers, SaaS applications, CI/CD pipelines, on-prem environments. The depth of context we can provide depends directly on how many of these systems we can connect to, and how well we pull data from them.

For enterprise customers, we're ingesting terabytes of data from dozens of sources. Massive volumes of audit logs. Every service account, API key, OAuth token, and certificate across their entire infrastructure, all processed through our ETL pipeline.

The problem: the value we deliver scales with integration coverage, but traditional integration development doesn't scale. At all.

So we built an AI agent to do it.

The Old Way Was Slow

An engineer would spend hours reading vendor API documentation, figuring out authentication schemes, mapping out which endpoints exist. Then they'd write the scaffolding: pagination handling, error recovery, rate limit management, retry logic. All the infrastructure that every integration needs but nobody wants to write for the hundredth time.

Then the actual fetcher code. Then schema mapping to transform vendor-specific data structures into our internal model. Then testing. Then code review. Then deployment.

For a moderately complex integration, this took days. Multiply that by the dozens of services our customers use, and you've got a serious engineering bottleneck.

None of this work is hard, exactly. It's time-consuming, it follows predictable patterns, and it pulls engineers away from problems that actually need human judgment.

What the Agent Does

We wanted to automate the tedious parts while keeping humans in the loop for critical decisions. The agent handles documentation parsing, code generation, data validation, and schema mapping. Engineers focus on edge cases, architecture decisions, and final approval.

The agent doesn't replace engineers. A task that used to require days of focused work can now be completed in hours, with higher consistency in the mechanical portions of the code. Our engineers are still doing the hard work; they're just not spending three hours reading Okta's API docs anymore.

It starts with model creation. The agent receives an integration name and gets to work: spinning up an isolated sandbox, reading and parsing vendor documentation to understand endpoints and authentication methods, then populating the sandbox with realistic test entities. If the target service requires registration or API key provisioning, a human steps in. We don't automate credential creation or acceptance of third-party terms of service.

Once the sandbox is ready, the agent moves to API interaction. It writes the fetcher code based on its documentation analysis and runs it against the sandbox, retrieving data at scale to validate functionality and performance. Then it checks completeness: no missing audit logs, no dropped attributes, no truncated records. Are all expected entity types present? Are relationships intact? Are timestamps and metadata complete? Given the volume of logs we ingest for enterprise customers, doing this manually across millions of entries isn't realistic.

The transform phase maps fetched data into our standardized schema. The agent runs the complete transformation pipeline against sandbox data, then uses DuckDB to query the resulting Iceberg tables. It's checking whether datatypes are correct, whether relationships are preserved, whether the data actually makes sense. Orphaned relationships, type mismatches, cardinality problems that would cause issues downstream.

Finally, human validation. The agent creates a pull request with all generated artifacts, sandbox configuration details, and validation results. A code review agent runs standardized checks for style compliance, security patterns, and documentation completeness. Then an engineer reviews the integration. Does the approach make sense? Are there edge cases the agent missed? Only after human approval does anything go live.

Continuous Monitoring

Integration development doesn't end at deployment. APIs change, vendors deprecate endpoints, authentication schemes evolve.

We run a daily validation job against all production integrations, detecting API breaking changes before they impact customers. If a vendor modifies their response format or deprecates an endpoint, we know immediately. Not when a customer reports missing data.

Sandbox-Only Development

The entire integration creation process runs on sandbox environments with synthetic data. Production customer data is never involved.

The AI agent never sees real customer data during integration development. All testing and validation happens against sandboxes populated with realistic but entirely synthetic entities. Each integration is developed in its own isolated environment, so there's no cross-contamination between different integration efforts.

Because the agent works only with synthetic data, we can let it explore and iterate aggressively without any risk of data leakage. This also makes compliance conversations simpler. Our AI-assisted development process operates entirely outside the boundary of customer data. Only after an integration passes all validation and receives human approval does it get deployed to production.

What Changed

Integration development that used to take days now completes in hours. The agent handles documentation analysis and boilerplate generation instantly.

Automated validation catches issues that manual testing might miss. The agent verifies data completeness in ways that would be tedious for humans to do consistently, and every integration follows the same patterns, uses the same error handling, maps to the same schemas. Our engineers spend their time on architecture decisions, edge case handling, and quality assurance instead of reading documentation and writing boilerplate.

What This Means for Customers

Faster integration development means faster time to value. When you deploy Clutch, the integrations you need are more likely to already exist. When you request a new one, the turnaround is dramatically shorter.

The consistency matters too. The same validation rigor applies to every integration, whether it's a major cloud provider or a niche SaaS tool. For NHI security, you need visibility across everything. This approach lets us get there.

Looking Forward

We've built real technical advantages across our platform: our unified NHI data model, our real-time correlation engine, our deep integration with identity providers. These are hard problems that require serious engineering work.

But not every engineering task needs a novel solution. Integration development involves a lot of repetitive work: reading documentation, writing boilerplate fetchers, mapping schemas, validating data completeness. Time-consuming tasks that follow predictable patterns. That's where AI agents shine.

By offloading this work to an agent, we free our engineers to focus on building new detection capabilities, improving our correlation algorithms, and expanding our security coverage. The agent handles the mechanical work. Our team handles everything else.

We're continuing to expand the agent's capabilities: better documentation parsing, support for more API patterns, refined validation heuristics based on what we're learning in production. We're building integrations at a pace that wouldn't have been possible a few years ago, and we're not compromising on quality to do it.