Table of Contents
AI agents are everywhere in today’s discourse—but what actually makes a system agentic? Let’s strip the jargon and break down the real mechanics.
Key Takeaways
- The term "AI agent" lacks a universally agreed definition, fueling both market hype and technical confusion.
- Definitions range from simple LLM-powered chat interfaces to systems approaching artificial general intelligence (AGI).
- Real agentic behavior involves reasoning, planning, tool use, and feedback loops—not just one-off LLM responses.
- Many "agents" today are effectively glorified wrappers over LLMs with added UI and automation logic.
- Pricing models for agents are often driven by perceived human replacement value rather than actual system complexity.
- Technical distinctions between agents and traditional SaaS are minimal—most agent apps resemble modern API-driven software.
- Data access and integration hurdles remain a major bottleneck for agents to reach their full potential.
- Multimodality and specialization will drive the next leap in agent utility, far beyond today's demo-level capabilities.
The Agent Confusion: One Word, Many Meanings
- “Agent” is now a bloated label. It describes everything from smart prompts to dreams of digital humans. There’s no singular technical definition.
- On the light end, some consider a well-crafted prompt layered over a chat interface an “agent”—nothing more than a dynamic FAQ bot.
- On the opposite end, others reserve the label for AGI-level systems with memory, planning, long-term autonomy, and tool integration.
- One panelist quipped, “Agent is just a word for AI application.” The problem? Even marketing teams sell it that way.
- A clean working definition emerged: something that interacts with external systems and performs complex planning. But this now applies to many LLM apps.
When everything becomes an agent, the term loses meaning. This conversation insists: let’s be precise about behaviors, not buzzwords.
The Anatomy of Agentic Behavior
- A defining trait of agents is a feedback loop—where output from one LLM pass becomes input for the next, enabling dynamic decisions.
- One panelist described this as a “multi-step LM chain with a dynamic decision tree”—not static prompting but evolving workflows.
- Planning and self-assessment (e.g., deciding when a task is complete) matter. Without them, it’s just a tool, not an agent.
- A recent definition from Anthropic: “An agent is an LLM running in a loop with tool use.” Not just inference, but iterative reasoning and action.
- But even this leaves ambiguity: does every CoT (chain-of-thought) prompt make something agentic? Probably not—context and intent matter.
What separates an agent from a function is often invisible to the end-user. Internally, though, agents combine multiple tools, steps, and adaptive logic.
Agents, SaaS, and the Illusion of Replacement
- Agents are frequently pitched as human replacements: $50K employees swapped for a $30K/year software “agent.”
- But this framing rarely reflects reality. More often, AI augments humans—slowing headcount growth rather than replacing people outright.
- “Two humans replaced by one AI-enhanced human” captures the real economic shift more than full-on automation.
- Agent tech is not replacing creativity or intent. Someone still pushes the button, sets the scope, or verifies the result.
- For now, many agents function like enhanced macros or mini-autonomous processes—not digital coworkers.
Selling AI like it’s a person isn't just misleading—it misses the value AI truly offers: productivity amplification, not wholesale substitution.
The Invisible Infrastructure Behind Agents
- Architecturally, agents are lightweight wrappers around LLMs and APIs. Most of the “agent” lives outside the LLM:
- Prompt orchestration
- Tool invocation
- State management via databases
- Output processing
- Most agent architectures could be replicated in any modern SaaS app—logic is lightweight, orchestration is the key.
- Non-determinism is a real challenge: incorporating LLM output into system control flows remains unsolved and risky.
- The compute intensity lies with the LLMs, often hosted separately. Agents simply coordinate these black boxes.
The biggest architectural gap isn’t performance—it’s trust and control over inherently unpredictable outputs.
Data Silos: The Agent’s Kryptonite
- Many consumer platforms don’t expose APIs, locking away data that agents need to act meaningfully.
- Data is power—and companies aren’t incentivized to give up control, especially if agents reduce user engagement.
- Web browsing agents struggle with CAPTCHA, auth walls, and login states, making them clunky and slow.
- Some services strip meaningful data from emails (e.g., Amazon hiding order info), rendering traditional scraping ineffective.
- There’s a darker side too: platforms actively deploying anti-agent measures to protect their ecosystems from automation.
Without open access, agents will forever be limited by their inability to act on your behalf across the digital world.
Multimodality and the Next Agent Frontier
- Current LLMs are largely text-based. But tasks like web browsing, drawing, or navigating interfaces require visual understanding.
- Future agents will need to process and act on images, buttons, vector graphics, and interactive UIs—not just text strings.
- There’s creative opportunity in fine-tuning: foundational models can’t cover everything. Stylization, niche tasks, and novel workflows remain wide open.
- One speaker noted: "Art is out-of-distribution samples." Agents won’t be artistic by default—they’ll need human-guided shifts.
- New tooling will emerge to let builders craft highly specialized, performant agents in vertical markets—from healthcare to anime illustration.
The next evolution isn't smarter general models—it's tailored agents fine-tuned to human aesthetics, tools, and tasks.
Rethinking Value: Pricing Agents in the Real World
- Agent pricing often follows the logic of replacement: “you’re paying for the human we no longer need.” But that’s shaky ground.
- True agent ROI comes from productivity—not personnel cost savings. The agent doesn’t need lunch breaks—but still needs supervision.
- As tech matures, pricing models will shift from speculative to usage-based and verticalized (e.g., coding agents vs. sales agents).
- SaaS margins remain attractive, but the floor is falling—thanks to declining compute costs and open model proliferation.
- Founders are figuring it out in real time. Most agents today are demo-level. The real money will come with purpose-built vertical solutions.
You’re not selling an “agent.” You’re selling time saved, workflows accelerated, and outcomes delivered
AI agents might not be the digital employees we imagined, but they are becoming essential collaborators. Their power lies not in mimicry of humans—but in their quiet, tireless efficiency.
And if we stop calling everything an "agent," we might finally start building systems that work like magic, not just marketing.
