Operations
We've inherited a lot of failed pilots. Engineering teams that spent three to six months building something, declared it a pilot, then stopped. The reasons vary in their specifics but are remarkably consistent in their structure.
This article describes the four failure modes we see most often — and what changes the outcome.
Most AI agent pilots are built to demo, not to deploy. The development environment is clean, the test data is curated, and the success cases are cherry-picked. The demo goes well. The stakeholder signs off. Then someone tries to run it on real data.
Real data is messier than test data in ways that are hard to anticipate. PDFs that aren't machine-readable. Emails that don't follow the expected format. API responses that return unexpected fields. User inputs that don't match the assumed schema.
A system built to demo will handle 80% of real-world cases reasonably well. The remaining 20% either fail silently, produce wrong outputs confidently, or crash in ways that are hard to debug. None of these outcomes are acceptable in production.
The fix: Build for the failure cases, not the success cases. Write test suites that specifically cover the edge cases and exceptions before the pilot starts. If you can't describe your failure modes, you haven't scoped the project correctly.
The question "is the agent working?" has no answer if you don't have an evaluation framework. This is more common than it should be.
An evaluation framework is a set of test cases with expected outputs. It's the same concept as a unit test suite, applied to LLM outputs. You run your inputs through the agent, compare the outputs to the expected outputs, and measure accuracy, latency, and failure rate.
Without this, you have no way to know when a model update degrades your system, when a prompt change improves or worsens performance, or whether the system is actually handling the input distribution you're seeing in production.
Teams that skip evaluation frameworks discover their problems when users complain — which is the worst possible time to discover them.
The fix: Write your evals before you write your agent. This sounds counterintuitive but it forces you to be precise about what success looks like before you start building.
A lot of pilots demonstrate that an agent can perform a task in isolation. The agent reads a sample document and extracts the right fields. The agent drafts a response to a sample email. The agent queries a sample database and returns the right result.
None of this tells you whether the agent can perform the task as part of your actual system — reading documents from your S3 bucket, drafting responses that go through your email API, querying your production database with your actual access controls.
Real production integration is substantially harder than demo integration. Authentication, rate limits, error handling, partial failures, data schema differences between the real system and the sample — these are where most of the engineering effort lives.
The fix: Build against production systems from the start. Using sample data and mocked APIs delays the discovery of integration problems until the worst possible moment.
Enterprise AI pilots often have no clear owner — a named person accountable for the system's performance in production. Without an owner, nobody monitors it, nobody responds when it degrades, and nobody escalates when it starts producing bad outputs.
This is partly a technology problem (systems without observability are harder to own) and partly an organizational one (AI systems require a new kind of operational discipline that most organizations are still developing).
The result is systems that run poorly for weeks or months before anyone notices, by which point the damage to user trust is significant.
The fix: Assign an owner before you deploy. Write a runbook that tells them what to monitor, what good looks like, and how to respond to specific failure modes. This is part of the production handoff.
All four failure modes share a common root: the pilot was treated as a proof of concept instead of an engineering project. A proof of concept is allowed to have shortcuts. A production system is not.
The organizations that successfully take AI agents to production treat the first deployment as a real product launch: with scoped requirements, measurable success criteria, integration testing against real systems, and an operational owner. The timelines don't end up being longer — they end up being faster, because you're not rebuilding everything after the pilot fails.
If you have a pilot that's stalled before production, book a scoping call. We've rescued several of these and know what it takes to get them across the line.