It’s March 2026, and I feel like I’m living in a perpetual state of “just around the corner.” Not the exciting kind, like a new coffee shop opening, but the kind where the future feels both imminent and perpetually out of reach. Especially when it comes to AI.
We’ve moved past the initial hype cycles, the breathless predictions of Skynet on Tuesday and utopia by Friday. Now, we’re in a murkier period. The AI models are incredibly powerful, no doubt. They write code, generate images, answer complex questions with alarming fluency. But they also lie, hallucinate, and occasionally make you wonder if you’re actually talking to a very sophisticated parrot. And that, my friends, brings us to the core of today’s rant: The Agency Gap – Why Our AI Tools Still Don’t Feel Like Agents.
For agntzen.com, we spend a lot of time dissecting what it means to be an agent. Intention, autonomy, responsibility, the ability to operate effectively within an environment. And while our current crop of AI models can mimic many of these facets, they often fall short in ways that highlight a fundamental disconnect. They are incredible tools, but tools nonetheless. They don’t *want* to do anything. They don’t *care* if they succeed. And that distinction, I argue, is becoming increasingly important as we try to integrate them into more complex workflows.
The Illusion of Intent: What We See vs. What Is
Think about your interactions with ChatGPT or Claude. You give it a prompt, it gives you a response. It feels like a conversation. It feels like it understands. But that “understanding” is a statistical prediction, not genuine comprehension. It’s a highly sophisticated pattern-matching engine, trained on vast amounts of human data, designed to produce statistically probable sequences of words.
A few weeks ago, I was trying to automate a tedious task: generating a series of slightly varied social media posts for different platforms, each needing a unique tone and character limit. I fed an AI model a persona for each platform and a core message. It did a decent job. But when I asked it to “make them sound more rebellious,” it just swapped out some adjectives. When I asked it to “think about what a punk rock band would tweet about this,” it gave me something that sounded more like a marketing intern trying to sound punk rock.
My human assistant, Sarah, if I gave her the same prompt, would probably ask clarifying questions: “Rebellious in what way? Politically? Aesthetically? Like a 70s punk or a modern one?” She’d draw on her own understanding of the world, her own experiences with punk culture (or at least, her ability to quickly research and synthesize it), and her own capacity for creative interpretation. The AI didn’t do that. It just processed the input and generated output based on its training data’s understanding of “rebellious.”
The Problem of “Good Enough”
This isn’t to say current AI isn’t useful. It absolutely is. It’s incredibly good at the “good enough” tasks. Draft a first pass. Summarize a long document. Generate boilerplate code. These are all things that used to take human effort, and now AI can churn them out at scale. But when the task requires genuine initiative, real-world understanding, or nuanced decision-making beyond what’s encoded in its training data, that’s where the agency gap becomes a chasm.
I was working on a small script to pull specific data from a public API. I gave the AI the API documentation and a clear goal. It generated a Python script that was 90% correct. The 10% was a subtle misinterpretation of how a specific query parameter should be formatted – a nuance that a human developer would quickly spot by reading the example requests. The AI, however, blindly followed a pattern it had seen elsewhere, leading to a frustrating series of 400 errors. It didn’t “realize” it was failing. It just executed the code it generated.
This is a critical distinction. An agent, when it fails, often has mechanisms to detect that failure, diagnose it, and adapt its approach. Current AI models, while they can be prompted to debug their own code or refine their outputs, don’t inherently possess that self-correcting, goal-driven loop in the same way. They don’t experience “frustration” or “confusion” that might lead them to rethink their strategy.
Practical Examples: Bridging the Gap (Imperfectly)
So, what do we do about this agency gap? We can’t imbue current models with genuine consciousness or desire (at least, not yet, and probably not in the way we think). But we can design our systems and our interactions to *simulate* more agent-like behavior, and crucially, build in human oversight and intervention points.
1. Designing for Iterative Refinement and Feedback Loops
Instead of treating AI as a “black box” that you give a prompt and expect a perfect answer, think of it as a junior assistant that needs clear instructions and feedback. This means breaking down complex tasks into smaller, manageable steps and building in explicit feedback loops.
Example: Content Generation Workflow
# Step 1: Initial Draft
ai_response = llm_model.generate_draft(topic="The Future of Quantum Computing", tone="academic but accessible")
# Step 2: Human Review and Specific Feedback
human_feedback = "The intro is too dry. Add a hook about a real-world application. Expand on the ethical implications."
# Step 3: AI Revision based on Feedback
revised_ai_response = llm_model.revise_text(ai_response, feedback=human_feedback)
# Step 4: Iteration...
# This loop continues until the human is satisfied.
This isn’t the AI spontaneously deciding to improve its introduction. It’s the human agent providing explicit instructions for improvement. The AI is a powerful tool for executing those instructions, not for autonomously deciding what “better” means.
2. Orchestration Layers for “Goal-Oriented” Behavior
This is where frameworks like LangChain or AutoGen come in. They don’t give the AI true agency, but they *orchestrate* a series of AI calls and tool uses in a way that *mimics* goal-oriented behavior. They create a meta-agent that uses individual AI models as components.
Imagine you want an AI to “find me the best flight from NYC to London next Tuesday, staying under $800, and include a hotel recommendation.” A single LLM call won’t do that reliably. But an orchestration layer could:
- Use an LLM to parse the request into structured queries (flight details, budget, hotel need).
- Call a flight search API with the extracted details.
- Call a hotel search API with the flight destination and budget constraints.
- Use the LLM again to synthesize the results into a readable recommendation.
- (Crucially) Have a human review the final recommendation before booking.
In this scenario, the “agent” is the entire system, with the human in the loop as the ultimate decision-maker and quality controller. The individual AI components are still just responding to prompts, but the system as a whole *appears* to be working towards a goal.
3. Explicit Guardrails and Human Override
For any system where AI is making decisions that have real-world consequences, explicit guardrails and easy human override mechanisms are non-negotiable. This isn’t just about preventing catastrophic errors; it’s about acknowledging the inherent lack of agency and responsibility in the AI itself.
Example: Automated Financial Advisor Bot (Hypothetical)
# Bot recommends a stock purchase based on market data and user's risk profile
recommendation = advisor_bot.generate_recommendation(user_portfolio, market_data)
# Crucial human approval step
if user_approves_purchase(recommendation):
execute_trade(recommendation)
else:
log_disapproval_reason(user_input)
# Potentially feed this back to the bot for future refinement (human-driven learning)
The bot doesn’t *care* if the user loses money. It just executes its programmed function. The human does care, and the system design must reflect that difference in agency and responsibility.
The Future of AI and Agency
I don’t think we’re on the cusp of true AGI, or at least not the kind that possesses genuine subjective experience and intention. The current trajectory of AI development, while impressive, feels more like building increasingly sophisticated mirrors rather than creating new minds.
What this means for us, as users and developers, is a responsibility to be clear-eyed about what these tools are and what they aren’t. They are phenomenal extensions of our own capabilities, amplifiers of our intelligence and creativity. But they don’t replace our agency. They don’t relieve us of the burden of responsibility. They don’t *want* anything. They don’t *believe* anything. They don’t *suffer* or *rejoice*.
The more we integrate AI into our lives and work, the more crucial it becomes to understand this agency gap. To design systems that use AI’s strengths while mitigating its weaknesses. To embrace the reality that the “agent” in “AI agent” often refers to the human orchestrating and overseeing the whole operation.
Actionable Takeaways: How to Work with Non-Agents
- Be Explicit: Don’t assume the AI understands nuance or context beyond what you’ve directly provided. Break down complex tasks.
- Build Feedback Loops: Treat AI outputs as drafts, not final products. Plan for iterative refinement based on human review.
- Design for Oversight: For any critical task, ensure there’s a human in the loop for approval, correction, or override.
- Understand Limitations: AI excels at pattern matching and prediction, not genuine understanding, intention, or common sense. Don’t ask it to do things it’s fundamentally incapable of.
- Orchestrate, Don’t Delegate Fully: Use frameworks and custom code to chain together AI capabilities and other tools. This creates a more agent-like system, but the human is still the architect.
- Educate Yourself and Your Team: The biggest risk isn’t AI becoming too smart, it’s humans overestimating its current capabilities and misattributing agency.
Let’s continue to build incredible tools, but let’s never forget where the true agency, the true responsibility, and the true spark of intention ultimately reside: with us.
🕒 Last updated: · Originally published: March 13, 2026