AI agents everywhere
Countless social media posts declare "agentic AI" as the next revolution. LinkedIn is flooded with people explaining how AI agents will transform every industry. Twitter threads proclaim the dawn of autonomous AI systems that will revolutionize work as we know it. And tech publications publish article after article attempting to explain "the difference between AI agents and agentic AI" - two colloquial phrases with no predetermined meaning.
We should be certain - the tech industry has perfected the art of repackaging existing technologies with new buzzwords in order to lure clients and to please venture capital. Today's AI agent frenzy follows this tradition perfectly. Companies are aggressively positioning themselves in this space:
From prototypes to products, the largest vendors are already shipping branded AI-agent frameworks that connect foundation models to everyday business software.
Salesforce Agentforce automates prospect outreach and objection handling across CRM data.
Microsoft’s Business and Industry Copilot agents draft research, build reports, and trigger workflows inside Microsoft 365. Google Cloud’s
Vertex AI Agent Builder lets enterprises publish domain-specific expert agents to internal app stores. ServiceNow AI Agents close HR or IT tickets end to end from within the Now Platform.
HubSpot’s Breeze Agents give small and midsize firms plug-and-play agents for knowledge-base support, prospecting, and content.
Amazon Bedrock Agents provide low-code orchestration so language models can call AWS services in multi-step processes.
Together, these offerings show that the step beyond ChatGPT is mainly tool wiring—powerful, but still engineering rather than emergence.What we're actually witnessing is the natural progression of technology development. When OpenAI shocked the world with ChatGPT in late 2022, we discovered these models possessed surprising reasoning capabilities. For developers and businesses, the logical next step wasn't revolutionary but evolutionary: connecting these models to other tools and systems.
Horse and carriage
Think of today's AI as a horse – intelligent, capable of understanding basic commands, and possessing natural abilities to perform useful work. What tech companies market as "agentic AI" is simply putting a harness on this horse and attaching it to a carriage. We haven't fundamentally changed the horse's nature; we've just found a practical application for its existing capabilities.
This analogy clarifies the situation perfectly. The horse (foundation model) already possessed the intelligence and strength. The innovation comes from harnessing these capabilities to pull the carriage (external tools and systems). The carriage provides structure and direction, while the horse provides the power. Together, they form a useful system, but there's nothing magical happening.
Yet the industry acts as if we've invented a new animal entirely. Marketing materials describe these "AI agents" as if they possess true autonomy and agency – qualities that remain firmly in the realm of science fiction. What we're actually seeing is careful engineering: creating structured pathways for models to follow, designing prompt templates that shape outputs in useful ways, and building integration points with existing software.
The horse and carriage worked together because humans designed the interface between them – the harness. Similarly, today's "AI agents" work because engineers design the interfaces between foundation models and external systems. The illusion of agency comes from carefully constructed pathways, not from any emergent capability of the underlying models.
When we strip away the marketing language, we find that "agentic AI" systems consist of the same ingredients: a large language model acting as the cognitive engine, specialized tools performing specific functions, integration layers connecting these components, and carefully engineered communication protocols. What changes are the specific tools, integration methods, and domains of application – not the fundamental nature of the technology itself.
Boring reality
From a technical perspective, what's happening is straightforward. Developers are creating frameworks that allow language models to interact with other software components – databases, APIs, search engines, and code interpreters. The models themselves haven't fundamentally changed; they're still prediction engines trained on massive text corpora.
What has changed is the infrastructure surrounding these models. By introducing structured interaction patterns, developers create the illusion of agency. A model instructed to check the weather by calling a weather API isn't suddenly "choosing" to use tools; it's following well-defined pathways created by engineers. The outputs may seem magical, but the underlying mechanisms remain transparent to those familiar with the technology.
These systems typically follow a predictable architecture:
A foundation model (like GPT-4 or Claude) processes user input and generates structured outputs
A "router" or "controller" interprets these outputs to determine which external tools to call
External tools perform specialized functions (searching data, retrieving information, executing code)
Results from external tools are formatted and fed back to the foundation model
The foundation model generates a final response incorporating this additional context
This architecture creates useful systems but doesn't represent any fundamental advance in AI capabilities. It's an engineering achievement built on existing components – similar to how early automobiles combined the internal combustion engine with carriage design principles to create something useful but not fundamentally new.
The limitations of these systems further reveal their engineered nature. They struggle with tasks requiring genuine understanding, fail in unpredictable ways when encountering edge cases, and require careful maintenance to keep functioning. These characteristics are typical of engineered systems, not autonomous agents.
What we're witnessing is not the birth of truly "agentic AI" but rather clever applications of existing technology. Developers are finding ways to compensate for LLM limitations through careful system design, essentially creating the appearance of advanced capabilities through integration rather than fundamental advances in the underlying technology.
What future holds
We need to reset our perspective on what constitutes genuine innovation in AI. The capabilities that impressed us in 2022 – reasoning, language understanding, knowledge retrieval – remain the fundamental breakthroughs. What we're seeing now is the natural application layer developing on top of these capabilities.
This doesn't diminish the importance of connecting AI models to external systems. These integrations will create tremendous value and solve real problems. However, framing these developments as fundamentally new types of AI obscures rather than clarifies. Let's recognize that we're in an engineering phase of putting existing technologies to work, rather than witnessing the birth of entirely new forms of intelligence.
The concept of "orchestration" offers a more accurate framework than "agency" for understanding our relationship with these systems. When we orchestrate AI systems, we acknowledge our role as conductors - coordinating different components toward a cohesive result. Orchestration recognizes the distributed nature of intelligence across both human and artificial components, working in concert rather than autonomously.
Effective orchestration requires understanding the capabilities and limitations of each component. It involves designing appropriate workflows where foundation models, specialized tools, and human judgment each contribute their strengths. This framework avoids the hype of "autonomous agents" while acknowledging the genuine value and complexity of integrated AI systems.
As these systems become more sophisticated, the orchestration challenge grows more complex. We must develop frameworks for coordinating multiple AI components, managing interactions between them, and maintaining human oversight. This requires moving beyond simplistic metaphors of "agents" toward a more nuanced understanding of human-AI collaboration.
By framing our progress as advancing orchestration rather than creating agency, we set more realistic expectations and focus on the practical challenges of system design. This perspective keeps humans at the center of technological development, not as threatened bystanders but as essential orchestrators directing increasingly powerful tools toward meaningful outcomes.