In the blossoming ecosystem of AI Agents and tooling platforms, the pitch sounds great: low-code, no-code, ready to deploy. But when you get into the build, the real story emerges. These systems aren't as turnkey as they seem.
If you’re a business leader wondering why timelines keep slipping, adoption’s stalled, or your “off-the-shelf agent” still needs two engineers and a week of debugging to function, here’s what your developers are probably not telling you directly, but wish you understood.
Reality Check: Building Agents Is Still Highly Technical
Most platforms frame themselves as intuitive and accessible. In practice, nearly every agent platform we’ve worked with from hyperscalers to emerging players requires real technical work to get anything production-ready.
Yes, there’s a GUI. But real functionality still hinges on:
- Python or JavaScript
- Access setup and secure permissions
- Tool configuration
- Custom endpoint development
- API troubleshooting
- Sparse or inconsistent documentation
This means a huge chunk of your business, including the domain experts who’d benefit most from agents, can't build or deploy them without developer support.
So it’s not as simple as “point, click, deploy.”
Where the Friction Lives
We’ve tested and built across all the major players. Here’s where the friction really shows up:
Documentation and API Gaps Slow Everything Down
Platforms are iterating fast, but often faster than their documentation can keep up. APIs are missing key reference information. Integration examples are vague or outdated. Most of our builds involved more reverse-engineering than actual instructions.
If you’re trying to move fast with a small team, this becomes a cost. A real one. Your developers will burn time debugging what should’ve been documented.
Tooling Is Where Agents Stop Being “Simple”
Every agent starts as a chatbot. It’s when you give it tools that it becomes powerful and complicated.
Where third-party tools were available, our developers tested thoroughly, ensuring reliability before deployment. In many cases, custom tool development gave us greater control, but also required more time and technical investment.
That “one-week setup” quickly turns into “two weeks of engineering just to make it usable.”
Agents Don’t Interoperate Across Platforms - Yet
Each vendor has their own way of defining what an agent is, how tools are managed, and how access works. Want to use Google’s agent for data prep, then hand it off to something on Bedrock? You’ll be rebuilding half the workflow to make it function.
This also sets the stage for vendor lock-in. The lack of easy cross-platform integration has led many teams to concentrate efforts on a single platform, not because it’s the best fit, but because context-switching between tools becomes a full-time job. While this simplifies operations in the short term, it can create constraints on future flexibility and scale.
Observability Is Inconsistent at Best
If you can't see what the agent’s doing, you can’t trust it and you definitely can’t govern it. Many platforms are still evolving their observability models. In several cases, we found it difficult to access granular logs or trace decision paths, making it harder for developers to confidently deploy agents in production. This is one of the most valuable areas for future improvement across the ecosystem.
You’re not just building a tool, you’re operationalising decision-making. You need a clear audit trail to do that responsibly.
So, What Should You Do About It?
If you’re committed to building with AI Agents (and you should be), you need to resource for the reality, not the brochure.
Here’s where we recommend focussing:
Be Open to Vertical
Agents aren’t meant to be generalists. The most productive agents we’ve seen are vertical, purpose-built, and scoped to specific operational goals. Focus on depth over breadth. Build the agents you need now, not a theoretical super-agent that handles everything.
Ask Hard Questions Before You Buy
Before you sign anything, ask your vendors:
- How frequently is your documentation updated?
- Can we see a sample API log?
- What’s the setup time for tools and system access?
- How does your platform handle observability?
These aren’t just nice-to-haves. They are the details your teams will depend on to build responsibly.
Focus on Jobs and Functions
Don’t get fixated on vendor brand comparison. Start with what you need the agent to do. What process is it replacing? What tools does it need? What system access is required? Then build backward from that.
You’ll avoid a lot of wasted integration time and you’ll ship something useful faster.
Design for Interoperability from Day One
Assume you’ll use more than one platform over time. Build for optionality. Don’t bet your entire AI strategy on one stack unless you’re willing to live with its limits.
Final Thought: Agents Work, If You Build for Them Properly
Agent platforms are maturing, but they’re not magic. They’re software systems, often with real complexity under the hood. The teams who acknowledge that early, who plan for the build, the governance, the integration, are the ones who’ll actually see ROI.
AI Agents are a force multiplier. But they’re not plug-and-play. Not yet.
As the ecosystem matures, we’re excited to collaborate with platforms and builders who are equally invested in responsible, high-performance AI Agents.
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