We asked 9 AI & agent builders about their top problems…
A deep dive into the real-world challenges that kill AI and agent projects before they reach production.
The AI agent revolution is here, at least in theory. Every week brings new demos of agents that can write code, book flights, and analyze spreadsheets. But there's a massive gap between a working prototype and a production system that can handle real users at scale.
Our team recently analyzed interviews with 9 engineers who've actually tried to productionize AI agents, from Apple to high-growth startups. We found the same set of problems keep killing projects, regardless of company size or technical sophistication.
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Here are the seven biggest pain points we found (ranked in order of severity):
1. The Cost Explosion That Kills Promising Pilots
The Problem: "Moving from 100 to 5000 users shoots the bill from $50k to half a million if we're not surgical," one enterprise AI lead told us. This isn't hyperbole, it's the reality of scaling AI agents.
Every API call, model token, and GPU second compounds. Teams that nail their proof-of-concept suddenly find themselves spending more time optimizing AWS instance costs than improving their agent's logic. One startup founder admitted: "We spent more time picking which AWS instance we could afford than tuning the model itself."
The Antidote: The teams that survive this transition implement three things early:
Smart caching to avoid recomputing identical requests
Model tiering that starts with cheap models and escalates to premium ones only when confidence is low
Granular cost tracking that monitors spending per request, not just daily totals
Don't wait until you're bleeding money to build cost controls. The most successful teams we’ve seen think intentionally about how they allocate spend and resources.
2. Debugging Across a Maze of Disconnected Tools
The Problem: "I'm stitching Bedrock, LangGraph, FastAPI…debugging across them is an 8 out of 10 pain," reported a principal ML engineer at a major enterprise. When an agent misbehaves, teams toggle between multiple dashboards, trying to piece together what happened.
There's no unified timeline. No single trace. Just scattered logs across vendor tools that don't talk to each other.
The Antidote: The teams with the least debugging pain invest in unified observability from the start. They log every action, input, output, and failure in a single, inspectable timeline. This is becoming essential for any agentic system that needs to handle real users.
One CTO put it perfectly: "If I get observability and our bill stays predictable, I'll rip out my home-grown glue tomorrow."
3. The Local Development Gap
The Problem: When building these systems, best practice is to leverage a workflow orchestrator. However, many orchestration tools force you to develop in the cloud, making it impossible to unit test locally.
Having to spin up a remote cluster drives up costs and slows iteration. This can mean death to projects with a tight budget and limited window to achieve production-readiness.
The Antidote: The best teams leverage tools that run similarly in local and production environments. If you can't reproduce a production bug on your laptop, you're setting yourself up for painful, costly debugging cycles.
4. When Static Workflows Meet Dynamic Reality
The Problem: Most workflow orchestrators expect you to define your entire pipeline upfront. But real AI agents need to make on-the-fly decisions based on live data. This dynamic decision-making represents the branching, looping, and adapting that make agents act with agency.
Traditional, linear DAGs break down when an agent needs to decide at runtime whether to query a database, call an API, or try a different approach entirely.
The Antidote: AI systems and agents require dynamic workflows that can reshape themselves at runtime. Look for systems that let you write normal Python control flow like if statements, for loops, and try-catch blocks.
5. Brittle Workflows and Agent Runtimes
The Problem: Unreliable, unrecoverable workflows are poison to sophisticated AI systems. When something goes wrong (and it will), outdated development tools force you to rerun entire pipelines from scratch. It's nearly impossible to track what inputs led to what outputs, making debugging a nightmare.
As workflows become more complex and longer running, the cost of re-running them becomes harder to swallow. Imagine a user waiting on a 15-minute deep research call. If that already long workflow fails and needs to restart, your user is going to feel the pain.
The Antidote: Crash-proof, recoverable workflows. Every task should be versioned, cached, and resumable. When a workflow fails, they can retry just the failed task and resume exactly where it left off, not from the beginning.
6. Legacy UI Slows Everything Down
The Problem: "Logs are buried. Failures are opaque. UIs are slow and confusing," was a common refrain. Teams spend hours hunting down problems that should take seconds to identify.
The DevEx found in rapidly aging devtools can bog down engineers so much that projects never make it to production.
The Antidote: AI orchestrators with the best DevEx make debugging simple by surfacing logs, inputs, outputs, and statuses with smart filtering and quick navigation. If your debugging process requires more than a few clicks to find a specific error, something is wrong.
7. The DSL Learning Curve Tax
The Problem: AI developer tools and orchestrators may use domain-specific languages that look like Python but don't behave like it. Engineers waste time learning new syntax and debugging unexpected behavior. This pain compounds as team members come and go.
The Antidote: Stick to native Python wherever possible. The cognitive overhead of learning yet another DSL rarely pays off, especially when you're already juggling multiple AI/ML tools.
The Bottom Line
Building AI systems and agents far beyond prompt engineering and model selection. Production-ready agents require the same operational rigor as any other distributed system, plus the unique challenges of managing non-deterministic LLM behavior at scale.
The good news? The teams that invest in proper orchestration, observability, and cost controls from the start consistently outperform those that try to hack fixes for these concerns later.
As one enterprise AI lead summarized: "Weeks of security reviews, memory tuning, daily retrains—that's a 10 out of 10 pain.”
But get the foundations right, and everything else becomes manageable.
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