Your AI agents need a watcher.
Ziplyne AgentOps is the first platform that lets one agent test another. We watch the agents you ship. We catch them before they catch you.
AI agents are everywhere. The QA layer is nowhere.
Every enterprise software vendor is now shipping AI inside their product. They take action. They make decisions. They talk to your other systems. Increasingly, they hand work off to each other without any human in the middle.
Here is the problem. These agents are not predictable. The same prompt does not always give the same result. They get updated weekly. They depend on configuration, on training data, on prompts, on whatever the platform vendor pushed last Tuesday. And the only people testing them are the same people who built them. That is not testing. That is marketing.
The vendor that builds the agent is grading their own homework.
There is no third party watching. No regression suite when prompts change. No way to validate that the workflow finished correctly. No visibility into what failed in production. The result is a category of enterprise software that is shipping faster than at any point in history, with less assurance than any other category in the building.
And this gets worse with scale, not better. The more agents you deploy, the more you expose. Every new agent is another decision being made without human review, another handoff that nobody validated, another surface where something can quietly go wrong. Intelligence without rules and rails is a dangerous blindspot.
Agents testing agents.
The only thing that scales with AI is more AI. Human QA teams cannot keep up with the rate at which agents update. Traditional test scripts cannot keep up with non-deterministic systems. And no single platform vendor will ever validate the agents that compete with theirs.
So we built Ziplyne Agent Testing, the ZAT. It is an agent whose only job is to watch the other agents. The ZAT sends prompts to your enterprise agents the way a real user would. It executes workflows the way a real user would. It compares what actually happened against what was supposed to happen. When something is off, it tells you immediately.
Deterministic validation on non-deterministic systems.
Workday Assist, Now Assist, Einstein, Joule, or your own. The agent runs a workflow inside your enterprise stack.
Same prompt, real workflow, grounded in DAP-captured baselines. Predictable validation on the non-deterministic system.
Pass, drift, or fail. Logged, audited, traced back to the recorded workflow. Operator gets the wheel the moment something is off.
RPA automates known steps. We validate behavior.
QA tests deterministic systems. We bring deterministic answers to non-deterministic ones.
We are not building another model. We are building the assurance layer above every model.
Four ways to test. One verdict that matters.
An agent failure is rarely simple. Sometimes the agent says the right thing but ran the wrong workflow. Sometimes it ran the right workflow but the data underneath was wrong. Sometimes the screen looks correct but the API call failed silently. Catching all of that takes more than one kind of test.
AgentOps validates four layers in parallel, every time:
We compare the agent's response to what a correct response looks like. Did it understand the request? Did it answer with the right intent? Did it use the right language for the right user? Anyone can read this output. You do not need to write code.
We compare what rendered on the screen to what should have rendered. Did the right form open? Did the right fields fill in? Did the right buttons appear? We even compare screenshots side by side, so a visual change shows up the moment it happens.
We follow the agent through the actual business process. Did it submit a purchase order, or did it just say it submitted one? Did it route to the right approver? Did it leave the customer record in the right state? We check the workflow, not just the words.
Behind the scenes, we check the API calls and data the agent created. Did it send the right values? Did it get a successful response? Did it leave the database in the shape your downstream systems expect? This is the layer that catches the silent failures, namely the ones that never reach a human until weeks later.
Finance teams build agents. Sales teams build agents. HR teams build agents. None of them write code. AgentOps lets the same person who built the agent also test the agent, by simply demonstrating what the right outcome looks like. No engineering ticket. No release window. No QA bottleneck.
What ships in version one.
If you are an engineer, a platform owner, or anyone who needs to know what is actually in the box, this is the section for you. The rest of the document speaks to outcomes. This section speaks to architecture.
Four validators. One verdict. Aggregated, weighted, traceable.
Four validators run in parallel on every test, namely text output validation using NLP similarity scoring and intent matching, UI validation using element presence and screenshot comparison, workflow validation that walks through step sequences and role-based access, and API validation that checks response structure and data correctness. Verdicts are aggregated, weighted, and tied back to the originating test case.
AgentOps runs tests in four modes, namely prompt-only when you want to validate just the agent's response, UI plus agent when you want full end-to-end coverage, API when you want fast deterministic CI checks, and hybrid when the workflow demands all three. Same test definition, different execution surface, picked at the test level.
AgentOps is built on the same DAP foundation Ziplyne has been hardening for years. Every workflow we already capture for in-app guidance becomes a real-world test corpus the moment AgentOps activates. There is no separate data collection step, no parallel infrastructure, no greenfield deployment. If you have Ziplyne DAP in production today, you can validate agents within days.
Workday Assist, ServiceNow Now Assist, MoveWorks, Salesforce Einstein, SAP Joule, and any custom-built enterprise agent. Test results route into Jira and Xray. CI / CD pipelines can trigger executions on every release. The architecture is platform-agnostic, so any new agent vendor that ships in the next twelve months is a configuration away from coverage.
Role-based access control, SOC 2 Type II compliant logging, data masking on sensitive inputs, single sign-on, SCIM provisioning. Every test run, every verdict, every operator action is logged with identity, timestamp, and reason code. Audit-grade. Tamper-evident. Built for the security review your CISO will run before approving deployment.
The platform that improves itself.
AI-generated test cases from existing DAP guides and RPA recordings. A failure diagnosis engine that auto-classifies what broke and routes it to the right owner. Self-healing tests that update their own selectors when UIs evolve. The roadmap turns AgentOps from a tool you operate into infrastructure that runs itself.
Orchestration is the moat. Nothing else is.
"Integration is table stakes. Intelligence is everywhere. Orchestration is what separates an enterprise that scales AI safely from one that scales chaos." The Ziplyne thesis
Every vendor in the AI testing space is racing on the same two things, namely better integration and smarter intelligence. Both will be commoditized in twelve months. Neither will determine who wins this category.
What wins is orchestration. The ability to coordinate, validate, and hold accountable agents that move across every app in the enterprise stack. Shared context. Single audit trail. Version control across releases. A kill switch that operators control. This is structurally hard to build. It requires sitting above the entire stack instead of inside any one platform. It requires the workflow capture only DAP-native vendors can produce. It requires the trust to operate across boundaries that platform-owned tools will never cross.
Necessary. Not differentiating. Connecting systems is the price of entry, not the product.
Without rules and rails, intelligence is a dangerous blindspot. Models without governance scale errors, not value.
Where agents are coordinated, validated, and held accountable across every app. This is what we own. Shared context. Single audit trail. Version control across releases. A kill switch operators control.
Trail every action. Identify every agent. Hold the security line. The infrastructure under everything else.
Read the table left to right. The first two columns are commoditized or already commoditizing. The fourth is the price of being taken seriously. The third is where Ziplyne wins, because it is the layer that requires everything we have already built and nothing the competition has.
The more you deploy, the more you expose. That is a math problem, not a hypothesis. Each new agent multiplies the surface area of decisions made without human review.
Without orchestration, every additional agent is another uncontrolled risk vector. With AgentOps in place, every additional agent is a validated, version-controlled, auditable component of the enterprise. The choice is not whether to deploy more agents. The choice is whether to deploy them with rails or without them.
When an agent does something you did not expect, what happens next?
Every agent runs three loops, no matter what platform built it. It senses what is happening. It decides what to do. It acts. Three loops, on repeat, thousands of times a day across your enterprise.
AgentOps is the control tower above those loops. We watch all three in real time. The moment an agent's behavior drifts from what it was supposed to do, the operator gets the wheel back. No incident channel. No 3 AM page. No engineer required.
We see what the agent sees, plus what the user sees, plus what the platform vendor cannot show you. Continuous observation across every ERP touchpoint and every workflow the agent traverses. Nothing happens off-camera.
We compare what the agent intends to do against what your captured workflows say it should do. We score behavior against the version-controlled baseline. The moment something drifts, we flag it. Before damage compounds. Before users feel it. Before the audit team finds it.
Pause the agent. Roll it back. Notify the operator. Or, if it passed every check, release it forward with the full audit trail intact. Action is automated, reversible, and logged. Operators stay in control without being in the hot path.
Pause. Kill. Contain.
Every AgentOps-validated agent runs behind a kill switch you control. The moment something drifts, surprises, or breaks, one click contains it.
The kill switch is not a marketing feature. It is a contractual control. Operators can pause individual agents, classes of agents, or the entire fleet. Every action is logged with operator identity, timestamp, and reason code, so the action itself is auditable. This is what an AI control tower actually looks like.
Three things AI needs to scale and be trusted.
"Get them right, you have built infrastructure. Get one wrong, you have built a liability that grows with every deployment."
Every conversation about enterprise AI eventually comes back to the same three conditions. We have heard them from CIOs, CISOs, audit teams, board members, and the engineers who have to make this work. They are simple to say and structurally hard to build.
If the workflow is not clear, no agent can execute it correctly. AgentOps starts where Ziplyne always starts, namely with the workflow itself, captured at the source through DAP, recorded as users actually run it. Without clarity of process, agents are guessing. With it, every test has a true north.
Agents that decide on bad data make confident, scaled, expensive mistakes. AgentOps validates the data flowing into agent decisions as carefully as it validates the actions coming out. Bad data plus a confident agent is the worst combination in enterprise software.
Cross-app coordination. Version control. Audit trail. Kill switch. The infrastructure that makes AI trustworthy at scale. This is where AgentOps is uniquely positioned, and where the rest of the market is structurally years behind.
Different reader. Same answer.
Whoever you are inside the organization, AgentOps changes a specific thing about the way you operate. Here is what changes for each of the people who will read this document.
You finally get an independent assurance layer over the AI agents your platform vendors are pushing into production. You stop relying on first-party self-attestation. You can deploy agents at the pace the business demands without taking on the risk that pace usually carries. You can answer the board's question about AI safety with something other than a hope and a slide.
Every agent action is logged with operator identity, timestamp, and reason code. Every test result is auditable. Data masking is on by default. You get the security posture you would demand of any other system this consequential, plus the kill switch that is not a marketing feature, namely the actual contractual control that lets you contain a runaway agent in one click.
When Workday or ServiceNow ships a new agent update on their schedule, you can run it through AgentOps before your users feel it. When a workflow change creates downstream drift, you see it in minutes instead of weeks. When a non-technical team builds an agent inside their tool, you have a way to validate it without becoming the bottleneck.
You can validate the agents your team builds without writing a line of code or filing an engineering ticket. You demonstrate the right outcome by clicking through the workflow once. AgentOps captures it and tests against it forever. Your team stays accountable for the agents they ship, without depending on a centralized AI platform team to clear every release.
AgentOps becomes a billable validation engagement on every AI deployment you run. It accelerates time to production for your customers because the assurance work happens in parallel with rollout. It opens a new revenue line that did not exist twelve months ago, and it positions your firm as the partner that ships agents safely instead of just shipping them fast.
This is not what you think it is.
AgentOps is genuinely a new kind of product. It pattern-matches to several things it is not, so it is worth being explicit.
- Traditional RPA. RPA automates known steps. We validate unknown behavior.
- Traditional QA. QA tests deterministic systems. We bring deterministic answers to non-deterministic ones.
- Traditional AI. We are not building another model. We are building the assurance layer above every model.
- An ERP feature. Platform vendors will keep shipping their own first-party assurance. None of it is independent. None of it crosses platforms. Ours does both.
- Agent vs Agent validation. The only architecture that scales with the surface area of enterprise AI.
- Process-aware testing. Validation grounded in the workflows your users actually run, captured through DAP.
- Deterministic validation on non-deterministic systems. The engineering problem we are uniquely positioned to solve.
- The control tower for enterprise AI. Sense, decide, act, with a kill switch the operator owns.
We have always been the platform that helps users adopt enterprise software. AgentOps makes us the platform that makes enterprise AI safe to deploy. Same foundation. Same DAP advantage. Categorically larger market. This is the move that takes Ziplyne from a feature company to an infrastructure company, and the window to claim that position is open right now.
Test your agents before they test your business.
30 minute private walkthrough. We will show you a live agent validation across two ERPs in real time, including the moment AgentOps catches drift. Engineering can request the technical brief and architecture deep-dive separately.
