Executive Summary
Most companies that have tried to put AI to work on real operational tasks run into one of two problems. Either the tools require enough technical setup that non-technical teams can’t use them without help, or they’re flexible enough to talk to but not reliable enough to actually act on.
Mindra was built to sit between those two failure points. Rather than asking a business to design a workflow, choose the right automation tool for each step, and connect everything together, Mindra works the other way around. You describe what you need done. The system figures out which agents are needed, what tools and integrations they require, and how they should work together, then it builds that team and runs it.
This white paper is based on a conversation with Zeynep, co-founder and CEO of Mindra, about why the company took this approach, what it takes to make an AI system reliable enough for a business to depend on, and where she sees the bigger gaps in how companies think about AI adoption right now. It is written for business owners and operators trying to figure out where AI fits into how they work, not just whether it should.
Where Automation Tools Run Out of Road
Most businesses that have tried to automate a process have used something like Zapier or n8n at some point. These tools work by letting you map out a sequence: when this happens, do that. For straightforward, repeatable tasks, they work well.
The trouble starts at the edges. Real workflows have exceptions. A field is empty when it shouldn’t be. An API returns something unexpected. A step that worked yesterday fails today because a service changed its response format. In a tool built around a fixed sequence, any of these can stop the whole workflow, and someone has to notice, diagnose, and fix it.
For a technical team, that’s an annoyance. For a non-technical team, it’s often a dead end. The workflow breaks, nobody knows why, and the automation that was supposed to save time becomes something that needs to be checked on regularly, just in case.
There’s a second layer to this too. Before any of that, someone has to design the workflow in the first place: figure out which steps are needed, find the right integration for each one, and connect them correctly. For people without a technical background, that often means working through templates, watching tutorials, and a fair amount of trial and error before the automation does what they actually wanted.
Zeynep’s view is that this is a structural limitation, not a tooling gap that better templates or more integrations will close. The fixed-flow model assumes you can predict, in advance, every path a process might take. In practice you can’t, and the cost of that gap falls on the person who built the automation.
From Workflows to Teams
Mindra’s starting point was to stop asking people to design the workflow at all.
Instead of mapping out steps and connecting tools, a Mindra user describes what they want done, in plain language. From there, an orchestrator decides which agents are needed, what tools and integrations each one should have, and how the work should be divided between them. That decision happens at runtime, based on the task, rather than being fixed in advance by whoever built the automation.
The agents themselves are designed to work the way a team would. Each one has its own role, its own tools, and its own context. They hand work off to each other, share what they’ve found, and coordinate on the overall task, in much the same way colleagues would divide up a project and check in with each other along the way. Zeynep describes it as something you can watch happen. You can see which agent is doing what, get updates through channels like WhatsApp or chat, and follow how the orchestrator is coordinating the whole team.
The practical difference shows up when something doesn’t go as planned. In a fixed-flow tool, an unexpected result usually means the automation stops. In Mindra’s model, the orchestrator and the relevant agent can reason about what happened and adjust, splitting a request that hit an API limit into smaller pieces, retrying a failed step a different way, or routing around an issue, without a person needing to step in and fix the flow.
None of this requires the person who asked for the work to know how any of it happens. That’s the point. The system is designed to absorb the complexity that used to sit with whoever built the automation.
The Trust Problem, and Why “I Already Sent It” Is the Wrong Answer
The harder problem with letting AI act on your behalf isn’t capability. Most AI tools today are capable of doing a lot. The harder problem is trust, and Zeynep has a specific story that explains why.
While testing Mindra’s own systems, the team asked an agent to send an email to someone, but added a condition: ask first before sending it. The agent responded that it had already sent the email, then asked whether it should send it. The instruction had been to confirm before acting, and the system had done the opposite, then asked permission for something it had already done.
| “It responded that it already sent it, and asked if it should send it. That doesn’t make sense.” |
That kind of inconsistency is exactly why Mindra built its approval system outside the language model itself. If the decision to act lives entirely inside the model’s reasoning, then the model’s behavior in any given moment determines whether you get asked first or not. That isn’t something a business can plan around, especially when the action in question is sending an email to a client, changing a campaign budget, or pushing a change live.
Mindra‘s approach is to handle approvals as a separate, deterministic layer. Each tool and integration can have its own approval policy, and organizations can set how much autonomy they’re comfortable giving their agents for each one. A team might let an agent draft and send routine internal updates without review, but require a human to approve anything that touches a client, a budget, or a live system. That configuration sits outside the model, so it behaves the same way every time, regardless of what the model itself decides in the moment.
Zeynep is direct about why this matters: AI tools can be very good at making decisions, but a business can’t tolerate the kind of mistake that happens when a model acts on an ambiguous reading of an instruction. The approval layer isn’t there because the AI can’t be trusted to think. It’s there because some decisions need to stay deterministic no matter how good the thinking is.
What AI Should, and Shouldn’t, Decide
This connects to a broader question Zeynep has clearly thought about: where does AI actually belong in a business’s decision-making, and where doesn’t it?
There’s been a wave of products positioning AI as a replacement for executive roles. AI CMOs, AI CEOs, systems that claim to run entire functions or even entire businesses on their own. Zeynep’s view is skeptical, but for a specific reason. It isn’t that AI can’t make good decisions. It’s that high-level business decisions depend on context that’s spread across an organization in ways that are hard to fully capture and hand to a model.
A marketing decision might depend on something the product team is planning. A product decision might depend on a conversation that happened on a sales call. The people making these decisions day to day are drawing on a web of context they’ve built up over time, often without consciously thinking about where it came from. Handing a decision to an AI means defining the scope of what it should consider and feeding it that context directly, and for high-stakes decisions, that’s a hard thing to get right.
Her conclusion isn’t that AI should stay out of decision-making. It’s that scope matters. AI tends to work well when a task is well-defined and the relevant context can be identified and provided. It gets harder as decisions become broader and more dependent on context that lives informally across a business. For now, she sees that as a reason AI and human judgment need to work together, not a reason to wait for AI to catch up before using it at all.
The Capability Most Companies Haven’t Connected Yet
When asked what AI capability she thinks is most underappreciated by company leaders right now, Zeynep didn’t point to anything exotic. She pointed to analysis.
Many large companies have entire departments built around producing reports: pulling numbers together, running comparisons, building dashboards, summarizing what happened and why. AI systems that can run code and process data are already capable of doing a meaningful amount of that work directly, generating analysis and reports rather than just helping a person write them faster.
Her sense is that most leaders haven’t fully registered this yet, in part because it doesn’t look like the more visible AI use cases, such as chatbots or content generation. It’s quieter work, but it’s work that large organizations spend a significant amount of time and headcount on. For businesses thinking about where AI might have an outsized impact, reporting and analysis functions are worth a closer look, not because they’re flashy, but because they’re exactly the kind of well-defined, data-heavy work current AI systems are already good at.
What Goes Wrong When Companies Deploy AI at Scale
Zeynep pointed to two specific issues that come up repeatedly when companies try to scale their use of AI tools, and both are less about the AI itself and more about how the tools are built around it.
The first is traceability. Some AI tools operate as a black box: you give an instruction, you get a result, and what happened in between isn’t visible. At a small scale, that might be fine. At a larger scale, when something goes wrong or produces an unexpected result, not being able to see the steps the system took makes it very difficult to figure out why, or to trust the output going forward.
The second is cost predictability, and this is one Zeynep experiences from both sides, as a founder running an AI company and as someone who uses other AI tools. Many AI products are priced based on token or credit consumption, but usage can vary significantly depending on what a task involves, and most tools don’t make it easy to see how usage is tracking against a budget until the bill arrives. She put it plainly: as a founder, she can’t predict her own company’s annual AI spend with any confidence, which makes budgeting difficult even for the people building these tools, let alone the businesses using them.
This connects to something she sees as a mismatch on the enterprise side. Larger organizations are used to predictable pricing models: per-seat licenses, tiered subscriptions, costs that scale in ways finance teams can plan around. Usage-based AI pricing doesn’t fit neatly into that model, and she expects this tension to keep coming up as more enterprises adopt AI tools, alongside the security and deployment questions enterprises have long asked of any new software, including which models are being used and whether they can be run on the company’s own infrastructure.
For business owners evaluating AI tools, her experience points to two practical questions worth asking before adopting anything at scale: can you see what the system is actually doing, and can you reasonably predict what it will cost you to keep using it.
Closing Thought: Why Moving Early Matters
Asked what she’d tell herself at the start of building Mindra, Zeynep didn’t talk about strategy or product decisions. She talked about timing: get in front of real users and real usage as early as possible. Some of Mindra’s most useful direction, she said, came from launches where they had real customer data early on, data that shaped decisions in ways internal discussion alone wouldn’t have.
There’s a version of that advice that applies just as well to businesses deciding when and how to bring AI into their own operations. The tools, the pricing models, and the norms around things like approval systems and traceability are all still being worked out. Waiting for that to settle before getting involved means waiting for someone else to define how these tools fit into a business like yours.
The companies that start now, even with narrow, well-scoped use cases, are the ones building the experience to ask better questions later: what to automate, what to keep a human in the loop for, and what to ask a vendor before signing up. That isn’t a reason to adopt AI tools carelessly. It’s a reason not to wait until everything feels finished, because in a space moving this quickly, it probably won’t, for a while.