The Real Limits of AI Agents in 2025

TL;DR: Everyone says 2025 is the year of autonomous AI agents. We’ve built a lot of them in production, and that’s exactly why we think most of the current hype just doesn’t add up. In this post, we’ll break down the most common misconceptions, talk about what actually works in the real world, and explain why the math and economics behind the hype don’t hold up yet.

Rumors and Speculations Breakdown

“Autonomous AI agents will replace traditional workflows in 2025!”
Not really. The idea of fully autonomous multi-step agents sounds great, but in practice it falls apart under simple math. The issue isn’t intelligence or prompt quality, it’s compounded error rates. Even small per-step mistakes grow exponentially over time, which makes true end-to-end autonomy impossible at scale.

“Conversational agents are the next big thing!”
Maybe, but not in the way most people think. Long-context agents suffer from quadratic token costs. Every new message has to reprocess the entire conversation, and that makes long sessions ridiculously expensive.

“We just need better APIs and the agents will figure it out!”
Nope. The real bottleneck isn’t model capability, it’s bad tool design. Most “AI agents” today fail because the tools they use don’t give them structured feedback. The AI doesn’t need human-style interfaces, it needs clean, machine-readable signals that help it reason about what just happened.

Engineering Reality Breakdown

Let’s move from the hype to the practical side: what actually happens when you build and ship AI agents in production.

1. The Mathematics Behind Failure

Error compounding quietly kills multi-step autonomy.
Say your model performs each step with 95% accuracy (which is already optimistic). Here’s what happens:

• 5 steps → 77% success

• 10 steps → 59% success

• 20 steps → 36% success

Real production systems often need 99.9% reliability. Even if you somehow reach 99% per step, you still only get about 82% success across 20 steps. That’s not a prompt issue, that’s just math.

On the contrary, our DevOps agent works precisely because it’s not truly autonomous. It runs 3–5 well-defined operations with rollback points and optional human confirmations. Each step is verifiable, and errors don’t pile up. The “autonomy” part is an illusion built on careful architecture.

2. Token Economics Nobody Mentions

There’s another uncomfortable reality: conversational agents are usually too expensive to scale.
Every new exchange requires processing the full conversation history, so token usage grows quadratically.

When we built a conversational database agent, the first few queries were cheap. By the 50th turn, each response can be costing several dollars, more than the value of the query itself. That doesn’t work in production.

That’s why stateless, single-turn agents are often more practical. Our function generator, for example, does one thing: it takes a description, produces a function, and stops there. No memory management, no exploding costs, just fast, cheap, and reliable execution. Ideally you want to validate and store intermediate results in conventional databases and at least try to guardrail them into being deterministic.

3. The Tool Design Wall

Even if you solve the math and the cost, there’s another wall waiting: tool engineering. LLMs are now quite good at calling tools, but the real challenge is designing tools that talk back in a way the AI can understand.

You need to think carefully about:

• How to report partial successes

• How to summarize large outputs without burning context

• How to recover when a tool fails

• How to handle dependencies between tools

Our database agent works well only because each tool returns structured, meaningful feedback, not just raw API dumps. That took weeks to get right. The truth is, the AI handles maybe 30% of the logic. The other 70% is the surrounding engineering: feedback design, context management, AI guardrails, error handling, and recovery mechanisms. All these mechanisms are trying to fit non-deterministic and unpredictable AI behavior into the strict frame reducing error rate drastically.

Integration Breakdown

And even if you fix everything else, you still need to connect your agent to real systems, and real systems are messy.
Enterprise software isn’t a collection of clean APIs. It’s full of quirks, legacy components, unpredictable rate limits, and compliance rules that change overnight.

Our production database agent doesn’t just “run queries on its own.” It manages transaction safety, connection pools, audit logs, and rollback logic — all the boring, reliable stuff you need to make things actually work. Integration is where most AI agents fail quietly.

What Actually Works

After building several different agent systems, a clear pattern has emerged. The ones that work all look surprisingly similar:

• UI generation agents succeed because humans review everything before deployment.
• Database agents work because potentially destructive actions require confirmation.
• Function generators work because they’re stateless and self-contained.
• DevOps agents work because they output infrastructure-as-code that humans can review and roll back.
• CI/CD agents work because the pipeline enforces strict success and rollback criteria.

And all these agents work only if you have clear and straightforward guidelines on a granular task you want them to perform. Just as you would explain something to a real person who never did it: with all the caveats and potential problems.

The pattern is simple: AI handles complexity, humans keep control, and traditional software ensures reliability.

Predictions for the end of 2025

Here’s how we think 2025 will play out:

• Startups chasing “fully autonomous agents” will hit a hard wall with cost and reliability. Few-step demos don’t survive real 20-step workflows. Real data and tools accessed via magic of MCP but without clear guidelines will not result in high accuracy even on simple few-steps pipelines.
• Big enterprise tools that just slap “AI agent” onto their existing products will stall because their integrations can’t handle the real world.
• The real winners will build focused, domain-specific assistants that use AI where it helps most, but still rely on humans or deterministic systems for critical control points and general AI agents guidance.

Eventually, people will realize the difference between AI that demos well and AI that actually ships. It’s going to be an expensive lesson.

Building the Right Way

If you’re building AI agents this year, start with these principles:

1. Define a clear problem you want to solve or automate. AI is not a magic box, its usage stays on the same principles as a classical software development. The only difference is that it can handle unstructured data with much less development effort and it has non-deterministic output.
2. Split the problem into verifiable pieces where possible. Instead of building a single complex agent make several small ones. If needed, make extra “manager” or “intermediate” agents that will aggregate results of several other agents.
3. Provide clean instructions. Each set of instructions should be clear and straightforward. Try to cover all cornercases, but not overthink it: if instructions become too complicated or too long then return to step 2.
4. Define clear boundaries. Know exactly what your agent can do and when it should stop. Be ten times more careful with agents that provide you with data and not just summarize and build reports. Do not give direct write access to the agent if it works with sensitive information.
5. Design for failure. Assume 20–40% of operations will go wrong. Have rollback plans. Always have a “ground truth” source of information which was not touched by AI at all. Have the full log of what was done in the system with an emergency script that can use your “ground truth” data to rebuild everything if needed.
6. Mind the economics. Measure token costs and scale realistically. Stateless often beats stateful. Cache agents responses if needed, especially if their job was to generate some intermediate data.
7. Prioritize reliability over autonomy. People trust consistent tools more than “magical” ones. Never deploy a new agent to a wide mass of people if you haven’t proved it to be effective. Use some beta-testers with expertise relevant to the application area to tune it up.
8. Use AI where it shines. Let it handle reasoning, intent, and generation. Give it unstructured data as input to work with. Leave data processing, execution and state to proven software patterns.

Outro

The agent revolution is real, but it’s not going to look like the hype suggests. The winning systems won’t be fully autonomous. They’ll be thoughtful combinations of AI reasoning, human judgment, and traditional engineering discipline.

We are not betting against AI. We are betting against the current obsession with its overpromising use. The real breakthroughs will come from teams who understand the limits, respect the math, and build around reality instead of wishful thinking.

Far-sight outlook:
Still, it’s worth thinking about where this all leads. Just like deep learning eventually replaced handcrafted pipelines with end-to-end systems, agents will likely follow the same path.

Over time, meta-learning and new reinforcement-learning methods — ones that don’t even exist yet — will let models learn not just tasks, but how to learn.

They’ll be able to adapt to feedback, handle rare edge cases, and self-correct in ways we currently have to hardcode. When that happens, the rigid guardrails we depend on today will turn into adaptive self-tuning mechanisms, and we’ll finally reach the true end-to-end agent era where you just add extra input as the system goes and it autocorrects itself accordingly without any additional inputs from your side.