Tech Talks Daily

How can businesses secure AI agents that read sensitive information, update systems and communicate with other agents on behalf of employees?
In this episode of Tech Talks Daily, I speak with Sachin Nayyar, founder and CEO of Saviynt, about AI agent identity security and the controls businesses need before autonomous systems enter production.
Saviynt manages over 100 million identities for over 700 customers. Sachin explains how enterprise identity has expanded beyond employees to include partners, applications, machines and autonomous AI agents.
An AI agent creates a different access problem because it is both an identity and an application. It can receive permissions, access information and perform actions, but its behavior can also be governed through software while it is being developed and while it is operating.
Sachin uses an HR copilot to demonstrate why context matters. Two employees can ask the same question about salaries but should receive different answers based on their roles, locations and applicable policies. Those decisions must be evaluated while the request is being processed without creating delays that make the system unusable.
The risk grows when an agent crosses from one technology environment into another. An agent built within Microsoft may need to access Salesforce, ServiceNow, Jira or another external system. Sachin warns businesses never to solve this problem by giving an agent a permanent administrative account.
We discuss Zuma, Saviynt's identity security platform for AI agents and non-human identities. Sachin describes a four-part framework beginning with agent discovery and a central registry. Every agent should then receive one accountable human owner, temporary access for its assigned task and policy enforcement while it acts.
Ownership becomes especially important when an employee leaves. Saviynt's approach begins an automated reassignment process and blocks actions if an agent attempts to operate without a current owner. The relevant security team can then investigate before allowing further activity.
Sachin also explains why identity controls should enter the development process rather than being added after deployment. Saviynt is working with LangChain and other agent development platforms to make identity policies available while AI agents are being built.
The conversation also covers Saviynt's partnership with Zscaler. Zscaler provides inline enforcement, while Saviynt contributes identity information about the agent, its owner, existing permissions and expected behavior.
Sachin closes with an optimistic argument. Because businesses can place security controls into the code and enforce them while agents act, AI workloads may eventually become better governed than traditional human access.
Could every AI agent inside your business be traced to one accountable owner, one approved purpose and a limited set of temporary permissions? Please share your thoughts with me.

What becomes possible when enterprise computer vision no longer depends on expensive GPU infrastructure?
In this episode of Tech Talks Daily, I speak with Glenn Jocher, founder and CEO of Ultralytics, about YOLO26, CPU inference, edge AI, open vocabulary vision, deployment economics, and the practical work required to move computer vision from a promising pilot into production.
Glenn's route into AI began inside the U.S. intelligence community. He worked with the National Geospatial Intelligence Agency and Defense Intelligence Agency on particle physics applications, attempting to detect and track antineutrinos.
Antineutrinos are extraordinarily difficult to detect because they pass through almost everything. Glenn describes them as the perfect spy. While searching for better detection methods, he discovered that computer vision researchers were solving similar problems with images.
His original attempt to transfer those techniques into particle physics did not succeed. However, the work introduced him to a field where the technology could create a visible effect on everyday life. That led him toward open source development and eventually the YOLO models for object detection, classification, segmentation, and tracking.
Glenn believes computer vision research has historically placed too much attention on small gains in accuracy while overlooking deployment economics. A model can perform impressively inside a laboratory and still remain unsuitable for a factory, warehouse, store, vehicle, drone, or medical environment.
Price, latency, power consumption, data privacy, and deployment speed can determine whether the technology is commercially useful. This led Glenn and Ultralytics toward smaller models capable of running close to where images and video are generated.
YOLO26 continues that approach with architectural changes designed specifically for CPU inference. Glenn says the model can process camera streams in real time at 30 frames per second and run across Intel CPUs, AMD CPUs, and lower power devices such as Raspberry Pi computers.
This matters because specialist GPUs can increase the equipment cost and power requirements of a computer vision project. Running inference on existing CPUs or edge hardware can make deployment economically possible across larger numbers of cameras and locations.
The scale already involved is difficult to comprehend. Glenn says Ultralytics models now process approximately three billion inference jobs each day, equivalent to around 30,000 every second. These jobs include images, videos, and collections of images being analyzed to detect, segment, or track objects.
He attributes the platform's maturity to thousands of mistakes and bugs corrected through a rapid feedback cycle. New models are released, users report problems and request features, and the team incorporates that information into later versions.
We also discuss the respective roles of cloud and edge infrastructure. Glenn sees cloud platforms continuing to provide the computing power required for training, while computer vision inference often belongs at the edge. Local processing can reduce latency, control operating costs, and keep sensitive video or medical information closer to where it was created.
The smallest YOLO model is approximately three megabytes, according to Glenn. That allows it to reach mobile phones, vehicles, drones, battery powered devices, and other environments where a large language model would be impractical.
Open vocabulary vision provides another development. Traditional object detection models are trained to recognize a fixed collection of objects. If a model learns to detect dogs and the user later wants it to detect cats, retraining can cause it to forget earlier knowledge unless both categories appear in the new training data.
Glenn explains how promptable models can identify common everyday objects from text or visual instructions without additional training. A user could request a person wearing a blue shirt and white shoes, for example, and the system could search an image for that description.
That flexibility could benefit businesses whose requirements change regularly. It reduces the need to create and label a new data set every time the company wants the model to recognize another common object.
The range of current applications is already extensive. Glenn describes YOLO being used across robotics, parking, industrial safety, PPE detection, warehouses, aviation, security, traffic management, food quality, and manufacturing.
Some of his favorite examples involve environmental problems. One company uses YOLO with underwater vehicles to identify and recover plastic from the ocean. Other applications detect smoke and fire early enough to support forest fire response.
For leaders considering computer vision, Glenn recommends beginning with a defined problem and measurable outcome. A manufacturing company may want to reduce defects, but it still needs labeled examples showing the model what acceptable and defective products look like.
He advises testing the idea through a limited pilot, measuring the return, and expanding only when the evidence supports further investment. Computer vision has become easier to deploy, but practical problems involving data, cameras, integration, reliability, and operating conditions still separate a demonstration from a production system.
Could CPU inference and open vocabulary models make computer vision practical for processes your organization previously considered too expensive? Listen to the episode and share your thoughts with me.
Useful Links
 
Ultralytics website
Ultralytics Platform

What can private companies learn from government caseworkers about adopting automation and using data more effectively?
In this episode of Tech Talks Daily, I speak with David Turner, General Manager and Senior Vice President of Government Services at Equifax Workforce Solutions, about public sector automation, data modernization, and the people responsible for delivering social services.
The conversation begins with a surprising finding from an Equifax Government Services study of over 500 U.S. government employees. Every respondent expected efficiency to improve during the following year, while 95% believed automation would free time for higher value, human centered work.
David explains why government employees may be more receptive to modernization than many people assume. Caseworkers operate under rising demand, staffing pressures, changing policy, and limited budgets. When technology removes repetitive administration or supplies information faster, they can see an immediate connection between the tool and the person waiting for support.
We discuss how the meaning of automation has changed inside social services. A few years ago, it might have meant entering information into an online portal. Today, integrated connections can search data sources behind the scenes and return verified information during the caseworker's existing process.
David describes continuous evaluation, which can identify when circumstances within a caseload have changed. Instead of searching every case for a possible update, a worker can direct attention toward the people whose income, address, or employment data indicates that further review may be needed.
Income verification provides another example. Equifax says it can return income information in under one second, helping prevent the delays created when an applicant must leave the process to find a document. Those pauses matter when an eligibility decision already involves several stages and numerous external systems.
The gig economy makes this work harder. Applicants may receive income from employment, contract work, digital platforms, and several side projects. Agencies need access to a fuller income picture without sending people back toward paper forms and manual verification.
David also shares what Equifax has learned through its Day in the Life program. The team spends time with caseworkers to understand their processes, policy restrictions, and information constraints. He believes public sector leadership can remain closely connected to employees in the field because many agency executives previously performed those roles themselves.
AI introduces further possibilities. David discusses how data and AI could eventually identify signs that someone who has left an assistance program may be experiencing financial difficulty again. Community groups, food banks, or other services could potentially offer support before that person returns to crisis. Such a system would also require careful decisions around consent, privacy, accuracy, and responsibility.
The central lesson is refreshingly human. Technology creates value when it removes administrative pauses and gives experienced caseworkers additional time to understand someone's circumstances.
Could public sector automation teach private companies how to connect technology investment with human outcomes? Listen to the episode and share your thoughts with me.

What happens when an AI experiment becomes a production service that your employees, customers, and daily operations depend upon?
In this episode of Tech Talks Daily, I speak with Brian Klingbeil, Chief Strategy Officer at Ensono, about AI infrastructure resilience, operational dependency, FinOps, legacy modernization, and the growing pressure to prove that enterprise AI investments are producing meaningful returns.
Brian has been speaking with major enterprises through Ensono's Executive Advisory Council. Three years ago, many participants were experimenting with proofs of concept. Today, they are being asked to present AI projects that are already in production, approaching production, or demonstrating a clear return through productivity, lower risk, service quality, or financial results.
That progression creates a new problem. When an AI model begins supporting product delivery, customer service, logistics, software development, or internal operations, it becomes part of the company's operating infrastructure. Leaders must then ask familiar IT questions about availability, monitoring, security, incident response, disaster recovery, ownership, and cost.
Brian believes FinOps often provides the first warning. Token consumption can be difficult for CFOs and business leaders to interpret, particularly when hundreds of agents are operating across different models. Ensono's internal platform has produced around 1,000 agents, prompting questions about which are effective, which are expensive, and who should carry the cost.
We discuss why chargeback and showback could change employee behavior. When AI spending is absorbed by a central corporate budget, teams may have little reason to question whether an expensive model is suitable for a routine task. When the cost reaches their departmental budget, the decision can look very different.
Architecture also matters. Brian recommends systems that are loosely coupled and tightly integrated. Companies should be able to replace a model, provider, FinOps tool, or service as the market changes, while still connecting each component closely enough to deliver useful business outcomes.
That creates a genuine tradeoff. Providers such as Microsoft, Amazon, Google, OpenAI, and Anthropic can offer specialist capabilities that businesses may want to use. Avoiding every provider specific feature can limit what the technology delivers, while becoming too dependent on one provider can make future change expensive and disruptive.
The conversation then turns toward legacy technology. Brian argues that many systems described as outdated still process airline reservations, banking transactions, insurance claims, government services, and other high volume workloads. Turning them off without suitable replacements would create far bigger problems than the word "legacy" suggests.
AI can change the modernization decision. Ensono worked with Markerstudy Group to analyze six million lines of RPG code running on an IBM i platform. The resulting plan identified applications that should move elsewhere while preserving workloads that still benefited from the platform's reliability and transaction processing capabilities.
Brian treats migration as one possible part of modernization. AI tools can document old code, support modern development environments, and allow younger developers to work with established platforms without immediately beginning a lengthy and expensive replacement program.
We also discuss Ensono's use of AI operations. Brian says the company reduced mean time to repair by 50% while processing approximately 50,000 tickets each month. The example shows how AI value can be measured through service quality and operational performance rather than relying entirely on direct revenue.
The result is a balanced conversation about moving quickly while building enough control to keep AI dependable. Organizations need space for experimentation, but production services also require ownership, budgets, recovery planning, and people who know what to do when something fails.
If one AI model or provider disappeared tomorrow, how much of your business would stop working? Listen to the episode and share your thoughts with me.

What happens when an autonomous AI agent makes a financial decision using inaccurate, outdated, or poorly synchronized data?
In this episode of Tech Talks Daily, I speak with Marcin Kaźmierczak, cofounder of RedStone Oracles and Credora Ratings, about why verifiable data is becoming so important to financial AI agents. RedStone originally developed its oracle infrastructure to supply smart contracts with reliable information from hundreds of sources. The same principles are now being applied as AI agents begin analyzing markets, recommending allocations, processing payments, and executing trades.
Marcin explains what a blockchain oracle does and why smart contracts cannot independently access real world information. RedStone aggregates, cleans, and distributes financial data, including asset prices, liquidity, volatility, and market capitalization. According to Marcin, its network currently secures over $10 billion in total value locked and has operated for six years without a mispricing or downtime event.
The discussion then moves into agent driven finance. AI agents can process information and act at considerable speed, but that speed introduces problems when the underlying information is delayed or the model hallucinates. Marcin describes the synchronization challenge created when an oracle updates every five seconds while an agent makes decisions every second.
One example captures the risk. An AI trading agent reportedly generated $200,000 over three months before losing $250,000 in two transactions. Marcin explains how Credora Ratings can add financial risk context by rating assets and strategies from D to A. Companies can then instruct an agent to operate only within an approved risk range.
We also discuss tokenized assets, the growing interest from major financial institutions, and why blockchain networks offer an attractive operating environment for autonomous finance. Marcin shares practical advice for leaders, including speaking with experienced implementers, testing agents in closed environments, identifying likely failure scenarios, and creating response policies before introducing real money.
What evidence would you require before trusting an AI agent with a financial decision? Listen to the episode and share your answer with me.