// Production / Agentic Workflows & RAG
Eval and observability for AI agents.
Your agent runs in production. A customer reports a bad answer. You can't tell whether retrieval missed, the model hallucinated, or a tool returned the wrong shape - because there's no trace, and the eval suite is three happy-path examples a teammate wrote. We wire the observability and eval layer that turns those incidents into something you can actually fix.
// What we see
The bugs you can't see are the bugs you ship.
01
You can't tell what step actually failed
A bad answer comes back. Was it retrieval, the planner, the tool that returned a malformed JSON, or the model summarizing badly? Without a trace per step you're guessing - and you're patching the symptom in a try/except, not the cause.
02
The bug rate is higher than the team thinks
After observability is wired, every team has the same week. The 'flaky' tool that fails 1 in 50 times is actually failing 1 in 8. The agent that 'usually works' truncates context on 22% of long conversations. The bugs weren't rare - they were invisible.
03
Every prompt change is a coin flip
Someone tweaks a system prompt to fix one customer complaint. Twelve other questions silently regress. Without an eval suite gating the merge, the team that improved it can't prove it - and the team that broke it doesn't know.
// What we do
Three layers that turn guesswork into engineering.
Most production agent reliability work doesn't need a new framework. It needs the trace layer, the eval set, and the CI gate - wired together so changes ship with measured deltas, not Slack messages.
Observability wired into your stack
Langfuse, LangSmith, Arize, or OpenTelemetry direct - we pick by your stack and self-host requirements, not vendor preference. Per-step traces, prompt and tool I/O captured, model versions and seeds attached. The trace becomes the canonical artifact your on-call reads at 2am.
Eval sets built from real traces
Eval cases come from production - real failures, real customer complaints, real edge cases - not from the happy paths someone imagined at scoping. We mine your trace store for the trajectories that broke, turn them into golden cases, and grow the suite as new failure modes surface.
LLM-as-judge calibrated against humans
LLM judges have 30-50% error rates out of the box - position bias, length bias, agreeableness bias, preference for their own style. We calibrate the judge against a human-graded sample (50-200 cases) and re-calibrate when the judge model changes. The number on your dashboard reflects reality, not the model's mood.
// Method fit
If you're shipping LLMs without evals, you're shipping blind.
skip it if
It's a throwaway demo nobody will use
Internal hackathon, two-day spike, paper draft - fine, a handful of cases in a notebook is enough.
For anything that touches a real customer, treat the eval suite as load-bearing.
use it if
Almost everything else. If a real user will touch the output, you need evals before they do.
Most agencies sell "AI integration" and skip the eval layer. That's how teams learn their failure modes from customer complaints instead of from a CI run.
We don't have a softer position. Observability week one. Evals from real traces by week three. CI gates by week six.
Without this layer you don't know if your system works. You just haven't been told it doesn't yet.
// How we work
Wire observability first. Mine traces for evals. Hand off the CI gates.
Every engagement starts by making the agent legible - traces per step, prompts and tool I/O captured. Once we can see what's happening, eval cases come straight from the failures the trace store reveals.
01
Wire observability into your stack
Langfuse, LangSmith, Arize, or OpenTelemetry direct - chosen for your framework and self-host requirements. Traces capture every step, prompt, tool I/O, model version, and seed. Your engineers get access to the workspace from day one.
02
Build the eval set from production traces
We mine the trace store for real failures and customer complaints, turn them into golden cases with expected trajectories or outputs, and add LLM-as-judge graders calibrated against a human-graded sample. The eval reflects what actually breaks, not what someone imagined at scoping.
03
Hand off the CI gates and the playbook
Eval suite runs on every PR. Regressions block merges. We hand off the runbook for adding new cases, the calibration procedure for the LLM judges, and Slack support for 30 days after delivery for the questions that come up after we leave.
// Expert insight
“I know you need to ship fast. I know LLM is smart, you checked it and it works. But belive me - if you skip them, you will create PoC in a week and "improve it" for the next year, fixing one thing while breaing the other. In bards.ai we treat evals as a main asset. This is our secret sauce of moving to prod so fast.”
Michał Pogoda-Rosikoń
Co-founder @ bards.ai
// Why bards.ai
Why us, instead of two senior agent engineers you'd hire.
You could hire the team. It would take a year and they'd learn the eval-and-observability stack on you. We've already learned it - on real engagements, with the LLM-judge calibration scars and the false-positive eval cases to prove it.
Production agent observability at scale
Brand24's internal agent unifies 13 data sources behind a Slack-native interface, sub-5s p50. The observability and eval stack we built around it is the same pattern we deploy on customer engagements.
Framework-agnostic, vendor-agnostic
Langfuse, LangSmith, Arize, OpenTelemetry direct, Phoenix - we wire what fits your stack and your data-residency rules, not what's on the vendor's home page. The methodology is portable across your future framework choices.
Senior engineers only, no juniors
Every person on your engagement has shipped agents to production and run their own incidents. No ramp-up tax, no learning the LLM-judge calibration story on your dollar.
// FAQ
Common questions about agent evals and observability
LangSmith if you're on LangGraph or deep in the LangChain ecosystem - it has the deepest framework integration, automation rules for routing low-quality traces, and online LLM-as-judge in GA. Langfuse if you're framework-agnostic, need self-hosting, or have data-residency constraints. Both run on OpenTelemetry under the hood; the methodology transfers either way. We pick by your stack and your security review, not vendor preference.
Out of the box, error rates are 30-50% - models prefer outputs in their own style, favor longer responses, and over-accept what they're shown. We always pair an LLM judge with a human-graded calibration sample (typically 50-200 cases) and re-calibrate when the judge model changes. After calibration, agreement with humans usually lands at 85-95% on most graders, which is good enough to gate CI.
If we mine eval cases from real traces, you usually catch the first regression in week one - often a prompt change someone made before our engagement that no one realized had broken something. New regressions get caught on PR if the cases cover that trajectory. The suite gets stronger every time a new failure mode surfaces and we add it.
Engagements start at $40K. Most agent eval-and-observability projects land between $40K and $90K depending on framework complexity, self-host vs SaaS, eval suite scope, and whether LLM-judge calibration is in scope. Fixed-fee proposal after the first scoping call - no time-and-materials surprise.
// Let's ship it
Send us a trace. We'll send back a plan.
Tell us about the agent, the framework, and the failure you can't explain. We'll come back with an observability and eval design - and the cases we'd mine from the first week of traces. Engagements from $40K, typically 4-6 weeks.
Michał Pogoda-Rosikoń
Co-founder @ bards.ai