---
title: "Would your AI agent get the job? A performance review framework for enterprise agents"
category: "talks"
date: "2026-06-29"
time: "11:40am-12:00pm"
track: "Expo Stage 4 SE"
room: "Expo Stage 4 SE"
speakers: ["Andreea Pleşea", "Dan Bălăceanu"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: ""
scheduleRoom: "Expo Stage 4 SE"
scheduleLabels: ["Expo Stage 4 SE", "session", "confirmed"]
---
# Would your AI agent get the job? A performance review framework for enterprise agents

## Conference Context
- Date/time: 2026-06-29 · 11:40am-12:00pm
- Track/room: track TBD · Expo Stage 4 SE
- Speaker(s): Andreea Pleşea, Dan Bălăceanu
- Session type/status: session · confirmed

- Track: track TBD
- Room: Expo Stage 4 SE
- Session type: session
- Status: confirmed

## Session Description
There are dozens of ways to build an enterprise AI agent: agentic frameworks, direct LLM APIs, conversational AI platforms, vertical SaaS. They all claim to do the job. But how do you actually compare them on the same task, with the same data, against the same KPIs? This session presents a vendor-agnostic evaluation framework that treats AI agents the way enterprises treat new hires: set the role, define success criteria, run candidates through identical scenarios, and measure outcomes. The architecture uses any LLM to track positive and negative drift across agents against weighted goals, monitoring everything from hallucination rates and token consumption to user sentiment and conversation quality. Inputs are standardized. Outputs are both quantitative (accuracy, cost, hours saved) and qualitative (tone, clarity). The methodology supports continuous evaluation, not just pre-deployment benchmarks, but ongoing performance reviews that can compare agent work against human baselines. Walk away with a concrete, repeatable process for answering the only question that matters: which agent actually does the job?

## Media Evidence
No related AI Engineer channel video found yet.

## Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

### Media Signals
No linked video, transcript, or slide source has been attached yet.

### Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

## Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

## People
- [[andreea-ple-ea]]
- [[dan-b-l-ceanu]]

## Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.

## Synthesis
### Synthesized Breakdown
# Would your AI agent get the job? A performance review framework for enterprise agents ## Conference Context - Date/time: 2026-06-29 · 11:40am-12:00pm - Track/room: track TBD · Expo Stage 4 SE - Speaker(s): Andreea Pleşea, Dan Bălăceanu - Session type/status: session · confirmed - Track: track TBD - Room: Expo Stage 4 SE - Session type: session - Status: confirmed ## Session Description There are dozens of ways to build an enterprise AI agent: agentic frameworks, direct LLM APIs, conversational AI platforms, vertical SaaS. They all claim to do the job. But how do you actually compare them on the same task, with the same data, against the same KPIs?

### Speaker And Company Context
- [[andreea-ple-ea|Andreea Pleşea]] — Co-Founder and COO at [[druid-ai|Druid AI]].
- [[dan-b-l-ceanu|Dan Bălăceanu]] — Chief Product Officer and Co-Founder at [[druid-ai|DRUID AI]].

### Topics Covered
- Topic links are pending transcript-backed classification.

### Derived Links And Source Material

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.