Category Robots Podcast

Automate 2025 takes place May 12th - 15th in Detroit, MI. The Exhibit hall floor will be loaded with new products and services. Here is a preview of some things to look forward to at this years event.

High-tech hospitality: Self-contained burger-making cell sets new benchmark in food service automation. IRB 360 FlexPicker® and YuMi® cobot join forces for flawless, hygienic, made-to-order meals.

While service robots with male characteristics can be more persuasive when interacting with some women who have a low sense of decision-making power, 'cute' design features -- such as big eyes and raised cheeks -- affect both men and women similarly, according to new research.

The hospitality industry can leverage the gender characteristics of service robots to influence customers' decisions, according to new research from a team in the Penn State School of Hospitality Management.

Palladyne™ IQ is a closed-loop autonomy software that uses artificial intelligence (AI) and machine learning (ML) technologies to provide human-like reasoning capabilities for industrial robots and collaborative robots (cobots). By enabling robots to perceive variations or changes in the real-world environment and adapt to them dynamically, Palladyne IQ helps make robots smarter today and ready to handle jobs that have historically been too complex to automate.

We’ve seen developers doing amazing things with Gemini 2.5 Pro, so we decided to release an updated version a couple of weeks early to get into developers hands sooner.

Our updated version of Gemini 2.5 Pro Preview has improved capabilities for coding.

Imagine AI-controlled robots that organize themselves into different groups, or across groups—and that reorganize themselves and make new plans when needed. This kind of flexibility can enable robots to effectively solve different types of tasks as a team.

Agentic AI is fast becoming the centerpiece of enterprise innovation. These systems — capable of reasoning, planning, and acting independently — promise breakthroughs in automation and adaptability, unlocking new business value and freeing human capacity. 

But between the potential and production lies a hard truth: cost.

Agentic systems are expensive to build, scale, and run. That’s due both to their complexity and to a path riddled with hidden traps.

Even simple single-agent use cases bring skyrocketing API usage, infrastructure sprawl, orchestration overhead, and latency challenges. 

With multi-agent architectures on the horizon, where agents reason, coordinate, and chain actions, those costs won’t just rise; they’ll multiply, exponentially.

Solving for these costs isn’t optional. It’s foundational to scaling agentic AI responsibly and sustainably.

Why agentic AI is inherently cost-intensive

Agentic AI costs aren’t concentrated in one place. They’re distributed across every component in the system.

Take a simple retrieval-augmented generation (RAG) use case. The choice of LLM, embedding model, chunking strategy, and retrieval method can dramatically impact cost, usability, and performance. 

Add another agent to the flow, and the complexity compounds.

Inside the agent, every decision — routing, tool selection, context generation — can trigger multiple LLM calls. Maintaining memory between steps requires fast, stateful execution, often demanding premium infrastructure in the right place at the right time.

Agentic AI doesn’t just run compute. It orchestrates it across a constantly shifting landscape. Without intentional design, costs can spiral out of control. Fast.

Where hidden costs derail agentic AI

Even successful prototypes often fall apart in production. The system may work, but brittle infrastructure and ballooning costs make it impossible to scale.

Three hidden cost traps quietly undermine early wins:

1. Manual iteration without cost awareness

One common challenge emerges in the development phase. 

Building even a basic agentic flow means navigating a vast search space: selecting the right LLM, embedding model, memory setup, and token strategy. 

Every choice impacts accuracy, latency, and cost. Some LLMs have cost profiles that vary by 10x. Poor token handling can quietly double operating costs.

Without intelligent optimization, teams burn through resources — guessing, swapping, and tuning blindly. Because agents behave non-deterministically, small changes can trigger unpredictable results, even with the same inputs. 

With a search space larger than the number of atoms in the universe, manual iteration becomes a fast track to ballooning GPU bills before an agent even reaches production.

2. Overprovisioned infrastructure and poor orchestration

Once in production, the challenge shifts: how do you dynamically match each task to the right infrastructure?

Some workloads demand top-tier GPUs and instant access. Others can run efficiently on older-generation hardware or spot instances — at a fraction of the cost. GPU pricing varies dramatically, and overlooking that variance can lead to wasted spend.

Agentic workflows rarely stay in one environment. They often orchestrate across distributed enterprise applications and services, interacting with multiple users, tools, and data sources. 

Manual provisioning across this complexity isn’t scalable.

As environments and needs evolve, teams risk over-provisioning, missing cheaper alternatives, and quietly draining budgets. 

3. Rigid architectures and ongoing overhead

As agentic systems mature, change is inevitable: new regulations, better LLMs, shifting application priorities. 

Without an abstraction layer like an AI gateway, every update — whether swapping LLMs, adjusting guardrails, changing policies — becomes a brittle, expensive undertaking.

Organizations must track token consumption across workflows, monitor evolving risks, and continuously optimize their stack. Without a flexible gateway to control, observe, and version interactions, operational costs snowball as innovation moves faster.

How to build a cost-intelligent foundation for agentic AI

Avoiding ballooning costs isn’t about patching inefficiencies after deployment. It’s about embedding cost-awareness at every stage of the agentic AI lifecycle — development, deployment, and maintenance.

Here’s how to do it:

Optimize as you develop

Cost-aware agentic AI starts with systematic optimization, not guesswork.

An intelligent evaluation engine can rapidly test different tools, memory, and token handling strategies to find the best balance of cost, accuracy, and latency.

Instead of spending weeks manually tuning agent behavior, teams can identify optimized flows — often up to 10x cheaper — in days.

This creates a scalable, repeatable path to smarter agent design.

Right-size and dynamically orchestrate workloads

On the deployment side, infrastructure-aware orchestration is critical. 

Smart orchestration dynamically routes agentic workloads based on task needs, data proximity, and GPU availability across cloud, on-prem, and edge. It automatically scales resources up or down, eliminating compute waste and the need for manual DevOps. 

This frees teams to focus on building and scaling agentic AI applications without wrestling with  provisioning complexity.

Maintain flexibility with AI gateways

A modern AI gateway provides the connective tissue layer agentic systems need to remain adaptable.

It simplifies tool swapping, policy enforcement, usage tracking, and security upgrades — without requiring teams to re-architect the entire system.

As technologies evolve, regulations tighten, or vendor ecosystems shift, this flexibility ensures governance, compliance, and performance stay intact.

Winning with agentic AI starts with cost-aware design

In agentic AI, technical failure is loud — but cost failure is quiet, and just as dangerous.

Hidden inefficiencies in development, deployment, and maintenance can silently drive costs up long before teams realize it.

The answer isn’t slowing down. It’s building smarter from the start.

Automated optimization, infrastructure-aware orchestration, and flexible abstraction layers are the foundation for scaling agentic AI without draining your budget.

Lay that groundwork early, and rather than being a constraint, cost becomes a catalyst for sustainable, scalable innovation.

Explore how to build cost-aware agentic systems.

The post How to avoid hidden costs when scaling agentic AI appeared first on DataRobot.

We leveraged our long heritage and leadership in robotics to create an AI software product that helps our customers overcome the challenges of traditional automation, accelerate set-up and deployment, and achieve greater ROI from their existing automation systems.

Researchers argue that the problem that has been lurking in the margins of many papers about touch sensors lies in the robotic skin itself.

Computer scientists have developed a new AI text-to-video model that learns real-world physics knowledge from time-lapse videos.

Researchers developed a framework to enable decentralized artificial intelligence-based building automation with a focus on privacy. The system enables AI-powered devices like cameras and interfaces to cooperate directly, using a new form of device-to-device communication. In doing so, it eliminates the need for central servers and thus the need for centralized data retention, often seen as a potential security weak point and risk to private data.

Researchers at Northwestern University and Israel's Tel Aviv University have overcome a major barrier to achieving a low-cost solution for advanced robotic touch. The authors argue that the problem that has been lurking in the margins of many papers about touch sensors lies in the robotic skin itself.

Featuring an integrated lifting plate, advanced navigation, and centralized fleet management, the OL-450S offers a complete solution for automating material transport in automotive, semiconductor and electronics, food and household goods, and other fast-paced industries.