AI Agents in the Enterprise | Sierra, Mercor, Intercom, Turing | $2.8B+ Raised

(We know the audio quality isn't great on this one :( But the conversation is still well worth it!)Last week I hosted a fireside chat on what it actually takes to build AI agents in the enterprise with Natalie Meurer (Head of Agent Eng, Sierra), Harsh Trivedi (founding engineer, Mercor), Juhi Parekh (GM, Turing), and Kevin Lynch (Senior FDE, Fin).We get into why new models aren't always better (and why you can't just swap in the latest release and assume your agent improves), how the data-labeling/RL environment business might only have a couple years left, why real-time voice-to-voice models still aren't production-ready, how cheaper inference is still causing prices to go up, how baking in a constellation of models into enterprise agents is so important for reliability,and much, much, more!Chapters below00:00 Intro00:21 Meet the panel01:57 What everyone's actually using agents for day to day06:10 The reality of forward deployed work09:28 What agents couldn't do a year ago that they can now12:29 Why you have to tell agents what NOT to do16:26 What a harness actually is22:03 RL environments explained28:46 Does the data-labeling and RL environment business even last?37:02 Why benchmarks don't tell you what works in production38:12 Agent engineering vs forward deployed engineering41:38 Deploying into 100-year-old enterprise systems44:25 Why AI adoption is an org problem, not a tech problem45:36 Hiring for judgment when engineers aren't really coding anymore48:16 Why agents are a new kind of software50:46 The first 90 days of an enterprise deployment53:20 Why compliance environments break normal testing56:58 Layering AI on AI to get to 99% accuracy01:00:56 New models aren't always better — the swap problem01:02:35 Improving agents without waiting for a new model01:06:36 Does agent performance secretly degrade over time?01:09:40 Why one model is never enough: the constellation approach01:11:14 Building resilience when inference providers go down01:13:48 When fine-tuning actually makes sense01:14:53 Why voice-to-voice still isn't production-ready01:16:25 The cascaded pipeline that real voice agents use01:21:45 Audience Q&A: managing change inside the enterprise01:23:24 Why inference getting cheaper makes things more expensive01:26:54 Charging for outcomes instead of conversations01:30:19 What the real moat is when everyone uses the same models01:37:04 Synthetic data and where the data wall actually is01:38:50 Closing thoughts