AI Agents are artificial intelligence systems that encompass the ability to design workflows independently in order to perform complex tasks. Given the available tools, AI Agents possess various functions such as decision-making, problem-solving, interacting with external environments and conducting actions. Read more below about the success and fluidity of AI Agents and how AI Agents PR is effective across various business structures.
Anthropic Drops ‘Workplace AI Agents’ Directly Inside Slack – AI News
The primary operational difference for these workplace AI agents is their capability to function asynchronously without real-time human prompting. If a network administrator activates the tool’s “ambient” configuration, Claude Tag monitors threads and tracks tasks autonomously. The agent checks inactive text threads, signals priority notifications from integrated software extensions, and tracks unresolved assignments across multi-day intervals.
Frankly, moving generative tools from individual sandboxes into persistent corporate communication channels presents distinct operational trade-offs. The clear upside is the optimisation of routine knowledge work. By centralising information logs directly inside active threads, companies can lower task friction, capture context across changing project teams, and reduce the time spent on manual codebase tracking or database updates. However, delegating cross-app workflows to background agents introduces significant structural risks for IT departments. Permitting automated systems to read chat histories, connect to email accounts, and modify central code repositories expands an organisation’s internal data-exposure risks.
Why AI Agents Are the Next Great Technological Transformation – Time
Going forward, AI agents will act increasingly independently. They will reason through multiple steps, draw on personal and environmental context, and take action—booking, coordinating, deciding, and adapting as conditions change. And they will do that by drawing on intelligence distributed across the device (or multiple devices), the network edge, and the cloud, depending on what the task requires and where it can be executed most efficiently. Consider what this means in practice. Instead of opening multiple apps to plan a trip, an agent could handle the itinerary, check your calendar, book the flights, and make calls on your behalf to adjust a conflicting appointment. AI agents could also replace many of the apps you use if you grant the agent the right credentials and permissions. On a PC, an agent could work across your files and applications to complete multi-step tasks, assembling reports, extracting data, organizing documents, and optimizing workflows.
Today’s smartphones, PCs, vehicles, smart wearables, and more were built for a world centered on apps and human-directed interaction. Now, agents also need to operate efficiently on your device. They must run continuously in the background, fuse sensor data into context, and orchestrate multi-step tasks reliably and securely. This requires strong CPU performance for orchestration, power-efficient NPUs for local models, and greater contextual awareness and efficiency—all while preserving responsiveness and battery life. These demands will drive an upgrade cycle across silicon and software, spanning device categories.
A Few Good Agents: Why Less May Be More In The AI World – Forbes
While the emphasis has been creating and unleashing an agent for every purpose, organizations are finding that adding skills is more productive. “Skills have turned out to be a more agile and smaller unit of currency,” said Maddie Want, vice president of data at Fanatics. Examples of more granular skills that can be extracted from existing agents include “codifying a particular piece of knowledge and sharing that across the org. The conversation we have now is does this need to be an agent, or is this just a skill? A lot of the time it’s just a skill.”
This scaling up in AI agent quality was evident at Fanatics, where Want oversees data engineering, data science, and machine learning for the company’s betting and gaming division. “Over time, the degree of investment we had to make in the context layer is decreasing,” she related. “And the degree of supervision an agent needs before its able to start autonomously answering questions is decreasing. And our ability to measure the accuracy of the answers is increasing. We can have more confidence in answers without looking.”
Future Of AI Depends On Agent Infrastructure – Forbes
While the term “AI agent” has become mainstream, most enterprise conversations still focus primarily on models, prompts and tool integrations. In practice, however, production-grade agentic systems require significantly more infrastructure around them to ensure reliability, security and operational control. An agent harness acts as the execution and governance layer surrounding AI agents. If the agent represents reasoning capability, the harness represents operational discipline.
This is especially important as enterprises adopt multi-agent architectures. Rather than relying on a single general-purpose AI system, organizations are beginning to deploy specialized agents for planning, retrieval, analytics, coding, compliance and workflow execution. These agents often need to collaborate while sharing context and respecting organizational policies. As complexity grows, the surrounding infrastructure becomes increasingly important because enterprise environments have far lower tolerance for unpredictable behavior than consumer applications.
Your Work Team Is Now a ‘Pod’ and Your Co-Workers Are AI Agents – Wall Street Journal
Smaller than a traditional engineering group, pods are designed to move faster to build and iterate on products. They’re also more cross-functional, including not just engineers but also designers and applied scientists. And critically, all that expertise is concentrated in just a handful of human workers (anywhere from one to eight), as well as AI agents.
For years engineering teams have been slowly favoring smaller and smaller teams in the name of speed and agility, but the growing capabilities of AI coding assistants and other agents that can potentially reduce the time-to-ship are allowing for even smaller pod-size structures. With AI agents doing more of the actual software development, including coding and testing, it takes fewer human workers to build products at scale.
As AI agents become employees, NewCore emerges with $66M to give them identities – TechCrunch
Companies are increasingly treating AI agents as workplace participants rather than software tools. Goldman Sachs last year tested AI coding agent Devin as a new employee, while McKinsey said earlier this year that 25,000 AI agents already work alongside its 60,000 employees. NewCore is betting companies will eventually need to manage those digital workers much like human employees.
NewCore’s platform is designed to manage both human and AI-agent identities in a single system. The startup says AI agents should be treated as first-class identities with their own permissions, life cycle controls, and revocation mechanisms, rather than as traditional service accounts or machine credentials.
Patients prefer healthcare providers’ AI agents to public chatbots, with human oversight non‑negotiable, survey finds
Patients are three times more likely to trust an AI agent when it’s embedded in a clinical system rather than offered as a public chatbot. It’s a clear signal that institutional accountability and provider context are central to acceptance of healthcare AI, according to Salesforce’s survey.
Today, 61% of global patients say they are comfortable using agentic AI in healthcare contexts, and 64% would share their full medical history with AI for a faster diagnosis. And that mirrors what’s happening on the provider side: a separate Salesforce study found 71% of U.S. healthcare workers predict agentic AI will be essential to healthcare operations within five years.
AI Agents Are Coming for the Instruction Manual – PYMNTS
The maker of Ninja kitchen appliances and Shark vacuum cleaners built the agent to replace the moment most customers dread: fumbling through a dense instruction manual trying to figure out where to start. The unboxing agent answers follow-up questions in context and surfaces product videos when a visual would help. A human is not involved unless the customer asks for one.
That shift points to something larger happening in enterprise AI. Most deployments have focused on answering customer questions faster and reducing pressure on call centers. The technology has been moving from chatbots that retrieve information to agents that guide customers through tasks, PYMNTS reported in April. SharkNinja’s agent is an early example of what that looks like in practice.
How can businesses innovatively use AI Agents PR?
FischTank PR is a top B2B tech PR firm with experience within AI (Generative AI, AI Agents, and more), enterprise tech, and other tech innovations. We guide forward-thinking companies in securing meaningful coverage, advancing key narratives and building brand awareness in a fast-moving industry where technological breakthroughs and innovations are accomplished daily.
If you’re interested in discussing how we can build out AI Agents PR initiatives for your organization, reach out to us at [email protected].
***News roundup guest post from FischTank PR interns Alexa Topolski and Julia Kindig***
