Why agents fail in production
An agent that nails a scripted demo is a different thing from an agent that survives a week of real traffic. Real users send inputs the prompt never saw. Tools time out, return half an answer, or change their schema without warning. A model upgrade you did not ask for shifts behavior in the middle of a multi-step plan. None of this shows up in a notebook run, and all of it shows up the moment money or trust is on the line.
The hard part is not the first version of the agent. It is keeping it honest as the inputs drift and the code changes. If you cannot see what an agent did and why, you are debugging blind. If you cannot measure whether a change helped, you are guessing. Most of the failures I have seen come down to those two gaps, so that is where we start.
What operating agents well takes
Running an agent properly is a small set of disciplines applied consistently. Evals come first. We build a suite of representative cases with clear pass criteria so that every prompt tweak, model swap, or tool change is judged against the same bar. When something regresses, you find out before your users do.
Tracing is the second piece. Every model call, tool invocation, and planning step gets recorded as a connected timeline, so a confusing run becomes a thing you can open up and read. Guardrails sit on the output path and on the inputs, blocking unsafe responses, catching obvious hallucinations, and validating that structured output matches the shape your downstream code expects.
On top of that, cost control tracks token spend per agent, session, and user, so you can spot the runaway loop before it shows up on an invoice. And for the decisions that carry real consequences, we put a human in the loop with a review queue and a clean interrupt, so the agent does the routine work and a person signs off on the rest.
What we build
We take agents from prototype to production and keep them there. For teams that already have something working, we wrap it in tracing, write the first eval suite, and add the guardrails and cost monitoring that let you ship changes without holding your breath.
For teams starting fresh, we design the agent and its tools, build the orchestration, and stand up the observability stack alongside it rather than after it. Either way you get a system your own engineers can read, extend, and operate, plus the dashboards and alerts that tell you when something needs attention.
How we work
We start with a short discovery call to understand the agent, the failure modes that worry you, and what success looks like for your users. From there we scope a first phase narrow enough to ship in a few weeks, usually tracing plus a baseline eval suite on your highest-risk flow.
After that we work in tight loops: a change, an eval run, a quick review with you, repeat. You see progress every few days, not at the end of a quarter. We keep our decisions and trade-offs in writing so nothing lives only in someone's head, and we hand off documentation as we go rather than scrambling to write it at the finish line.
Why FoundrySoft
We are a small team of senior engineers in India who do this work directly, not a shop that sells you a partner and staffs the build with juniors. The people you talk to are the people writing the code. We keep daily overlap with US business hours, so you get same-day answers and live working sessions instead of overnight round trips.
You own everything we produce under a full IP assignment, and we move in weeks rather than months. That combination, senior hands, real time-zone overlap, clean ownership, and fast delivery, is the whole reason teams bring us in for agent work.
Frequently asked questions
What is AgentOps?
AgentOps is the practice of building, deploying, and operating AI agents in production. It covers everything that keeps an agent reliable once real users depend on it: evaluations that catch regressions, tracing that explains why a run behaved the way it did, guardrails that stop unsafe output, cost controls, and human review for the decisions that warrant it.
Why do AI agents fail once they reach production?
Most agents work in a demo and break under real traffic because the inputs get messier, the tool calls fail in ways the prompt never anticipated, and small model changes ripple through multi-step plans. Without traces you cannot see where a run went wrong, and without evals you cannot tell whether a change made things better or worse. We treat both as first-class from day one rather than bolting them on after an incident.
Do you work with US clients across time zones?
Yes. Our engineers in Chennai keep several hours of daily overlap with US business hours, so you get same-day responses, live working sessions, and standups that land inside your morning. We schedule deliberately around your team rather than handing off into a black hole.
Who owns the code and models you build?
You do. Every line of code, every prompt, every eval suite, and every model artifact we produce is yours under a full IP assignment. We document the system so your team can run it without us, and we are happy to hand off cleanly when that point comes.
How long does an AgentOps engagement take?
We measure delivery in weeks, not months. A typical first phase puts tracing, a baseline eval suite, and core guardrails around an existing agent in two to four weeks. Building a new production agent from scratch usually runs four to eight weeks depending on the number of tools and the review requirements.
Ready to make your agents reliable?
Tell us what your agent does and where it breaks. We will tell you what a first phase looks like and how quickly we can ship it.