Agents That Feel the Room—and Fix Themselves
Where agentic AI is headed, and why intelligent data flywheels matter
If you watch today’s agents for more than a few minutes, you see the mood swings. In one chat they’re warm and crisp; in the next they over‑explain, miss an obvious cue, or bungle a tool call with the wrong parameter. And even when there’s no “human feel” at all—just a heads down task—they drift off spec: skip required steps, optimize the wrong objective, or simply neglect or misinterpret human inputs.
I don’t think this is a “just scale it” problem. It’s a feedback problem. We’re still treating behavior—tone, choices, task adherence, even API hygiene—like vibes in a prompt instead of something we can observe, grade, and steadily improve. That’s why I keep coming back to intelligent data flywheels: small, concrete loops that turn context ↔ behavior trace into a living artifact for training, evaluation, and live steering.
What I mean by “intelligent data flywheels”
The picture in my head is simple. Keep a map of the situations your agent encounters (support vs. sales, calm vs. upset, low‑stakes vs. high‑stakes) and the behaviors you want in each (clarity, empathy, brevity, caution, brand voice and values). Use that map to (a) generate realistic multi‑turn data—by having a “Human LLM” act out the human side of conversations—and (b) judge the agent against the behavior playbook with a grader that you also train. Then run the same primitives at three speeds: observability (find where it breaks or excel), inference‑time nudging (steer it right now), and training (make the fix stick). When human raters and the judge disagree beyond a reasonable margin, you update the judge; when they align, you let the judge carry more load and feed the next enhancement round. That’s the loop.
I still care a lot about emotional intelligence—human-facing agents that communicate across different media and modalities—but the same flywheel helps with boring, high‑impact stuff that isn’t “EQ” at all: tool use, retrieval grounding, latency/cost tradeoffs, and safety drift.
Why this suddenly feels practical
A few research threads clicked into place:
Principle‑following reward models showed you can align behavior to a collection of human-written rubrics instead of massive preference sets
Inference‑time scaling for judges matured (e.g. DeepSeek’s GRM). Spend more tests‑time compute—parallel samples plus a meta‑judge—and you get more reliable reward signals for both training and live guardrails.
Judges that actually reason generate a case-specific rubric before scoring, which humans can verify, align, and generalize..
Open, strong preference data (e.g. helpsteer3) and closed, experiential data from your deployed agent provides a solid base you can specialize with your own behavior map.
Caveat: LLM‑as‑a‑judge isn’t magic. Benchmarks like JudgeBench show judges can be brittle or biased if you don’t treat them like first‑class products—versioned, monitored, retrained, and contexted.. That’s another reason to put them inside the flywheel.
The unglamorous stuff agents fail at
Function calling & schema correctness. Even top models still fumble basic format rules (quote this string; ISO date there) and multi‑step tool chains. Recent work—BFCL, JSONSchemaBench, IFEval‑FC—quantifies how often calls are syntactically valid yet semantically wrong. In my head, the “judge” can be a schema/trace checker with scenario‑aware penalties, and the generator can synthesize tricky, long‑horizon tool graphs to close the gap.
Grounding & hallucinations in RAG. Datasets like RAGTruth and newer lenses like HalluLens keep reminding us that extrinsic hallucinations haven’t vanished; high‑certainty hallucinations are especially sneaky. A flywheel can grade answers on entailment against retrieved context and choose the next hard cases to label or synthesize.
Open‑world task reliability. Real agent work looks like OS‑level workflows and the messy web. OSWorld, WebArena, and AgentBench have moved the bar here and highlight recurring failure modes—state tracking, planning depth, visual grounding. Using their task taxonomies as “contexts” and step‑level success as “behaviors” gives you a clean contract for the flywheel to optimize.
Safety and the “persona dial.” OpenAI’s emergent‑misalignment results point to interpretable persona features—directions in activation space that modulate toxic or deceptive modes. That turns safety from a black box into a dial you can monitor and counter‑steer inside the flywheel.
And yes, EQ still matters because humans are in the loop. Benchmarks like EmoBench, EmotionQueen, and multimodal EmoBench‑M show a persistent gap to humans on “understand + respond appropriately.” That’s the sweet spot for a behavior‑by‑context map coupled to a judge that also reasons about emotion.
How this looks in practice—my mental movie
I picture an analytics view that doesn’t just say “CSAT dropped,” but where and why: “In escalations from upset users, brevity overrode clarity; schema errors spiked in step‑3 tool calls; the judge drifted on empathy.” From there, the loop suggests more of what it needs: a batch of synthetic escalations with complicated tool chains; a judge tune on Emotion‑Application items; a tweak to the behavior weights for this scenario. We close the loop in three places: surface the issue (observability), compensate now (inference), and make it permanent (training). Publish the change, re‑benchmark, repeat.
Over time you get a system that not only thinks better, but behaves predictably under stress—because behavior stopped being vibes and started being data.
And one more thing: over time, those results compound into a differentiated, market-adapting playbook — a living operational memory co-built by your team and the system, shaped by every success, failure, and fix.