Agentic AI

When the term agentic AI is used today, it often conjures images of individual, autonomous systems making plans, taking actions, and learning from feedback loops. But what if intelligence, like biology, doesn’t scale by perfecting one organism — but by building composable ecosystems of specialized agents that interact, synchronize, and co‑evolve? That’s the thesis behind Agentic Design of Compositional Machines — a sprawling, 75‑page manifesto that reframes AI architecture as a modular society of minds, not a monolithic brain. Drawing inspiration from software engineering, systems biology, and embodied cognition, the paper argues that the next generation of LLM‑based agents will need to evolve toward compositionality — where reasoning, perception, and action emerge not from larger models, but from better‑coordinated parts. ...

When we talk about AI in science, most imaginations stop at the screen — algorithms simulating molecules, predicting reactions, or summarizing literature. But in LabOS, AI finally steps off the screen and into the lab. It doesn’t just compute hypotheses; it helps perform them. The Missing Half of Scientific Intelligence For decades, computation and experimentation have formed two halves of discovery — theory and touch, model and pipette. AI has supercharged the former, giving us AlphaFold and generative chemistry, but the physical laboratory has remained stubbornly analog. Robotic automation can execute predefined tasks, yet it lacks situational awareness — it can’t see contamination, notice a wrong reagent, or adapt when a human makes an unscripted move. ...

TL;DR Agentic performance isn’t just about doing more; it’s about going back. In GSM-Agent—a controllable, tool-using version of GSM8K—top models only reach ~65–68% accuracy, and the strongest predictor of success is a high revisit ratio: deliberately returning to a previously explored topic with a refined query. That’s actionable for enterprise AI: design agents that can (1) recognize incomplete evidence, (2) reopen earlier lines of inquiry, and (3) instrument and reward revisits. ...

If your agents can reach more valuable futures with fewer steps, they’re stronger—whether you measured that task or not. Today’s paper offers a clean way to turn that intuition into a number: empowerment—an information‑theoretic score of how much an agent’s current action shapes its future states. The authors introduce EELMA, a scalable estimator that works purely from multi‑turn text traces. No bespoke benchmark design. No reward hacking. Just trajectories. This is the kind of metric we’ve wanted at Cognaptus: goal‑agnostic, scalable, and diagnostic. Below, I translate EELMA into an operator’s playbook: what it is, why it matters for business automation, how to wire it into your stack, and where it can mislead you if unmanaged. ...

TL;DR Most reflection frameworks still treat failure analysis as an afterthought. SAMULE reframes it as the core curriculum: synthesize reflections at micro (single trajectory), meso (intra‑task error taxonomy), and macro (inter‑task error clusters) levels, then fine‑tune a compact retrospective model that generates targeted reflections at inference. It outperforms prompt‑only baselines and RL‑heavy approaches on TravelPlanner, NATURAL PLAN, and Tau‑Bench. The strategic lesson for builders: design your error system first; the agent will follow. ...

Thesis: The next leap in practical web agents isn’t bigger models or deeper search trees—it’s a tight loop that learns by failing well. Recon‑Act’s two‑team architecture (Reconnaissance → Action) turns mistakes into generalized tools and feeds them back into execution. That’s not just a benchmark trick; it’s an operating system for enterprise‑grade automation. Why this matters (for operators, not just researchers) Most “browser LLMs” still thrash on real websites: ambiguous DOMs, mixed text‑image signals, fragile flows, and long horizons. Recon‑Act reframes the problem: when progress stalls, stop trying harder—learn smarter. It does three things companies can copy tomorrow: ...

TL;DR Agentic LLMs can translate legal rules into working software and audit themselves using higher‑order metamorphic tests. This combo improves worst‑case reliability (not just best‑case demos), making it a practical pattern for tax prep, benefits eligibility, and other compliance‑bound systems. The Business Problem Legal‑critical software (tax prep, benefits screening, healthcare claims) fails in precisely the ways that cause the most reputational and regulatory damage: subtle misinterpretations around thresholds, phase‑ins/outs, caps, and exception codes. Traditional testing stumbles here because you rarely know the “correct” output for every real‑world case (the oracle problem). What you do know: similar cases should behave consistently. ...

If autonomous agents are the new employees, your bearer tokens are their keycards. Today’s OAuth/JWT keycards open too many doors for too long, and no one can prove why a door was opened—only that it was. This is fine for deterministic apps; it breaks for stochastic, tool‑calling LLM agents. Agentic JWT (A‑JWT) proposes a surgical fix: bind every API call to a cryptographically verifiable intent (and optional workflow step), and give each agent its own identity plus proof‑of‑possession (PoP) keys. Zero‑Trust, but practical. ...

TL;DR Most teams still analyze pipelines with brittle SQL, custom scripts, and static dashboards. A new reference architecture shows how schema-driven LLM agents can read workflow provenance in real time—across edge, cloud, and HPC—answering “what/when/who/how” questions, plotting quick diagnostics, and flagging anomalies. The surprising finding: guideline-driven prompting (not just bigger context) is the single highest‑ROI upgrade. Why this matters (for operators, data leads, and CTOs) When production AI/data workflows sprawl across services (queues, training jobs, GPUs, file systems), the real telemetry isn’t in your app logs; it’s in the provenance—the metadata of tasks, inputs/outputs, scheduling, and resource usage. Turning that exhaust into live answers is how you: ...

If you’ve ever tried turning a clever chatbot into a reliable employee, you already know the pain: great demos, shaky delivery. AgentArch, a new enterprise-focused benchmark from ServiceNow, is the first study I’ve seen that tests combinations of agent design choices—single vs multi‑agent, ReAct vs function-calling, summary vs complete memory, and optional “thinking tools”—across two realistic workflows: a simple PTO process and a gnarly customer‑request router. The result is a cold shower for one‑size‑fits‑all playbooks—and a practical map for building systems that actually ship. ...