Quick Demo of CoAgent:

AI Agents in the wild

It's 2am. Your AI agent broke in production. Again.

Users are complaining that responses don't make sense. Your CTO wants to know what happened. You open your monitoring dashboard and see... everything looks fine. Latency is normal. No errors. The model is responding.

Token usage is increasing steadily. You might receive the OpenAI Tokens of Appreciation, the milestone awards for OpenAI API token usage! But you might lose your customers. And die the slow death that awaits every startup!

Something is clearly wrong.

You spend the next four hours digging through logs, trying to reconstruct what happened. Was it the user's question? The context you fed the model? Did the reasoning go sideways? You can't tell. By the time you find the issue, a subtle context degradation that started three days ago, it's morning and you haven't slept.

This is what shipping AI blind looks like.

The Gap Between Prototype and Production

Here's what nobody talks about: building an AI feature is easy. Making it work reliably in production is hard.

The prototype phase feels magical. You wire up an LLM, feed it some context, get impressive results. Your demo goes well. Everyone's excited. You ship it.

Then reality hits.

In production, things get messy. User questions you never anticipated. Edge cases that break your carefully crafted prompts. Context that degrades, or worse, rots over time. Reasoning that works 90% of the time but fails catastrophically the other 10%. And you have no idea which 10% until users tell you.

The stats bear this out. MIT found that 95% of GenAI projects don't make it to production. Gartner says 40% of current AI initiatives won't get funding next year. These aren't failed experiments. These are teams that built something that worked in testing but couldn't make it reliable at scale.

Why?

The Blind Spot

Most teams approach AI the same way they approach traditional software: build it, monitor it, fix what breaks.

But AI applications don't break the same way software breaks.

Traditional software fails predictably. A function throws an error. A database times out. You get a clear signal something is wrong.

AI fails quietly. The model responds. No errors. But the response is... off. Maybe it misunderstood the user's intent. Maybe the context was incomplete or too much. Maybe the reasoning took a wrong turn. The system technically worked. It just didn't work well.

Your monitoring tools tell you the AI is running. They don't tell you if it's working.

This is the blind spot. You can see latency, token counts, costs, model versions, F1 scores, precision, recall, accuracy. You can't see:

• Why the AI misunderstood what the user wanted

• Where the reasoning broke down

• If it passed your domain-specific validation rules

• What the user actually did with the response

• Whether it drove the business outcome you care about

So when things go wrong, and they will, you're debugging between smoke and mirrors.

What's Actually Needed

To ship trustworthy AI, you need visibility into the complete chain:

User intent → What is the user actually trying to do?
Agent context → What information did we give the AI?
AI reasoning → How did the model process this?
Domain validation → Does this pass our business logic?
User action → What did the user do with the response?
Business outcome → Did it drive the result we care about?

Generic monitoring shows you the first step executed. It doesn't show you if the chain worked.

And here's the thing: "working" isn't generic. It's domain-specific. And it's not deployed and done. It requires continuous calibration.

A betting recommendation needs to validate against odds accuracy and risk rules. A financial document parser needs to handle citations without corrupting numbers. A resume screening tool needs to map "product" to "company" for validation. These aren't generic quality checks, they're contextual business logic.

You need controls that let you define what "working" means in your domain, test against it, and trace when things don't match.

Enter CoAgent

We built CoAgent because we kept seeing smart teams hit the same wall.

They'd ship AI features fast. Get them into production. Then spend 60% of their time debugging issues they couldn't see. The tools that worked for traditional software monitoring like logs, metrics, traces weren't enough for AI operations.

CoAgent gives AI engineers the controls to test and monitor AI applications end to end. Not just "is it responding" but "is it working in our domain."

Here's how it works:

You define what matters. Not generic accuracy scores. Your domain-specific validation rules. What does a good response look like for your users? What business logic must it follow? What outcomes should it drive?

You trace the complete chain. From the moment a user asks something through the AI's reasoning process to the action they take and the business result. When something breaks, you see exactly where.

You test dynamically. Not just static input-output tests. Test during live conversations. After three turns of back-and-forth, is the conversation still in the right space? Are tool calls making business sense? Is user intent being tracked correctly?

You catch issues early. Context degrading slowly? Responses getting fuzzier? You see it before users complain. Set up domain-specific alerts that fire when things drift from your baseline.

You capture what experts know. When your domain expert corrects the AI—fixes a label, adjusts a categorization—that correction doesn't disappear. It becomes part of your evaluation dataset. The system learns from human feedback.

All of this runs on your infrastructure. Your data stays in your network. It works with whatever tools you're already using, AWS, GCP, whatever SDKs, whatever backends.

What This Enables

When you can see what's actually happening, everything changes.

Debugging shifts from hours to minutes. You don't dig through logs hoping to find the issue. You find the specific pressure points where things went wrong.

Testing becomes comprehensive. You're not just checking if the model responds. You're validating against your specific business logic at every step.

Quality stays consistent. You catch degradation before it impacts users. Context corruption, reasoning drift, validation failures—you see them early.

Trust builds over time. You can demonstrate exactly what's working and what's not. Not with generic dashboards, but with domain-specific metrics tied to business outcomes.

And most importantly: you ship confidently. Not because you hope nothing will break, but because you can see what's happening and fix issues fast when they do.

The Path Forward

AI applications and agents promise to transform how we work. But transformation requires trust. And trust requires reliability.

You can't build reliable AI without visibility. You can't have visibility without domain-specific controls. And you can't implement those controls while debugging blind.

CoAgent gives you the foundation to go from "it works in the demo" to "it works in production at scale."

We're starting with teams who've already shipped AI features and hit the reliability wall. SaaS companies who invested millions in GenAI and now need to prove it works. AI consultancies building for clients who demand production-grade systems. Engineering teams who are tired of firefighting and want to actually build.

If you're shipping AI and finding yourself debugging more than building, we should talk.

The future of AI isn't just better models. It's better operations. Controls that let you ship fast without breaking trust. Visibility that turns guesswork into certainty.

That's what we're building.

Why are "We" building CoAgent

For the past six years building distributed streaming infrastructure: Fluvio and Stateful DataFlow, think Kafka and Flink rebuilt in Rust. Before that, we worked on service meshes, autonomous systems, industrial IoT, network security monitoring. Different domains, same challenge: how do you process massive amounts of data reliably at scale?

When GenAI took off, we watched engineering teams hit walls we'd seen before. Debugging blind because they couldn't trace through systems. Quality degrading from context corruption they couldn't see. Silent failures because monitoring was built for requests, not reasoning chains.

We recognized these as foundational software problems, as much as they are foundational data problems, and foundational model problems. The patterns were familiar in some ways. And they are specific in

So when teams asked "how do we monitor AI agents reliably?" we knew how to answer. Not because we were AI researchers, We're not. But because we're software engineers who deployed production AI / ML / predictive analytics at scale.

You can't build reliable AI without operational software, just like you can't build distributed systems without traces and metrics.

We built distributed systems in the past. Now we're building the operational layer AI needs to go from experiments to production.

That's CoAgent for us. Rigorous software engineering applied to AI operations. So that we can enable AI Engineer to