RELEASE THESIS
AI agents need a release process.
Agents are moving from demos into real workflows. They call tools, retrieve documents, update records, route users, and touch money. But many teams still ship changes with manual checks and memory.
Known failure
image request routed to text response
Expected
send_product_image
Actual
ai_agent_text_only
The demo is not the hard part.
The first version of an agent can look impressive. It answers, calls tools, and completes a workflow. The harder question comes later.
Software learned this lesson already.
Software teams do not rely on memory before release. They use tests, reviews, CI, staging, approvals, rollback, and logs.
Agent teams need their own release process. Not copied blindly from software, but built around how agents fail: language, tools, routing, retrieval, policy, and behavior.
Text evals are not enough.
A user asks to see a product image. The agent replies with a correct text description. The answer sounds fine. But the workflow should have sent an image.
The failure is not only in the final text. It is in the system behavior.
TAQ is built around this belief: check what the agent did, not just what it said.
Failures are the best tests.
A real failed run contains the best signal:
Every serious agent team will build a memory of failures. The question is whether that memory lives in Slack threads and human anxiety, or becomes a release gate.
The first wedge is regression testing.
TAQ starts with a narrow wedge: failed agent runs become replayable regression tests. Before a new prompt, model, tool, retrieval setup, or workflow ships, known failures replay.
If the old mistake comes back, the release is blocked.
The larger release layer.
If AI agents become as common as apps and APIs, every company will need a release process for them. We want to make TAQ the default release layer for AI agents.
That means more than tests over time. It means approvals, policies, audit trails, release history, rollback, incident memory, and confidence before agents touch customers, data, tools, or money.
This is the direction. The first wedge is known failures becoming release gates.
What we believe
Old agent failures should not reach users twice.
A failed run should become a test.
A release gate should check behavior, not only text.
Teams should know what changed before an agent ships.
Agents need release control before they touch customers, tools, data, or money.
Final CTA
Show us one failure you never want users to see again.
Send us what the user asked, what the agent or workflow did, and what should have happened instead. We will help turn it into a replay case.