From 7 Tools to 1: Why We Built AgentLift

The seven-tool problem

If you’re building AI agents in production today, your stack probably looks something like this: a framework for agent logic, a separate service for tracing, another for evaluations, a prompt management tool, a cost tracking dashboard, a deployment pipeline, and something for A/B testing or rollback. That’s seven tools minimum, each with its own API, its own dashboard, its own billing, and its own set of abstractions.

Why existing tools aren’t enough

Each of these tools is good at what it does. LangSmith is great for tracing. Braintrust is excellent for evals. But the moment you try to connect them, you hit friction. Your traces don’t link to your evals. Your cost data lives in a different dashboard than your quality metrics. Rolling back a bad deployment means coordinating across multiple systems. The integration tax is real, and it compounds as your agent fleet grows.

The runtime advantage

We realized the fix wasn’t building a better version of any single tool — it was rethinking the architecture entirely. By owning the runtime where your agent executes, AgentLift can automatically capture traces, attribute costs, enforce quality gates, and manage deployments without requiring you to instrument anything. It’s the difference between bolting sensors onto a car after it’s built and designing them into the chassis from day one.

One platform, zero compromise

AgentLift replaces the fragmented stack with a single platform that handles deployment, observability, evaluation, and cost management together. No more context-switching between dashboards. No more integration glue code. Just deploy your agent and let the platform handle the rest. Get started today.