Companies across industries are moving quickly to build agentic AI systems. But getting an agent to work in a prototype is very different from getting one to perform reliably in production. Once agentic AI systems encounter real-world requirements around speed, accuracy and cost – the trade-offs become much harder to ignore.
Across the many agentic AI projects I have worked on, one expectation comes up repeatedly: teams want the responsiveness of modern SaaS applications delivered through agents. It’s achievable, but not without trade-offs, as agentic AI systems have certain limitations and have not fully evolved. Discussing these upfront can save considerable redesign later.
From my experience building these systems, there are five challenges I recommend considering before you build an agentic system.
Challenge 1: Some agentic apps need SaaS-level speed
Agentic AI initiatives often include specific expectations of how the systems should perform. The problem starts when those systems are expected to respond like search boxes. At first glance, these demands might sound reasonable. But these expectations can be difficult to reconcile with the way today’s agentic systems operate. Agentic AI includes steps like plan, tool call, check the result, retry and verify. All these steps bring in accuracy, but they add to latency. That creates a fundamental trade-off between responsiveness and the additional reasoning needed for reliability.
What works: Conversations about any agentic AI project should start with a clear outline of the trade-offs involved. Start with both options: what 2-second responses get you and what 20-second responses get you. Then, move to designing once you have decided which side to take. Otherwise, the outcome is bound to disappoint.
Challenge 2: Untold trade-offs among speed, accuracy & cost
Optimizing speed, accuracy and cost simultaneously is extremely difficult. But misconceptions about agentic AI and overly aggressive vendor benchmarks can reinforce that expectation. Optimizing an agentic system is a negotiation between these three factors. Winning in two is a good deal. But this is only possible when the architectural decision is more intent-driven and you are clear about what you can trade off with.
What works: Start by setting up a base parameter for your accuracy. Then, optimize the model for speed and cost. In practice, when you optimize for latency and cost, you lose in accuracy. If you have a fixed base, then while optimizing the other two parameters, you will have an idea about how far you can go. In fact, by introducing proper checks and confidence scoring, you may not lose in accuracy.
Most of the hosted model providers provide a server-side caching option referred to as prompt caching. Once we ensure all static prompt content is at the beginning of the prompt, agentic framework functionality can be used to enable prompt caching on the server side. This helps in reducing cost by a big margin.
Challenge 3: Bigger isn’t better; it’s just a different trade-off
There’s nothing wrong with opting for bigger models. They have more parameters and better reasoning, which means the chances of making mistakes are lower – but that comes with higher latency and cost. A smaller model isn’t a downgrade. Smaller models are great for near real-time chat, basic FAQs and anything that is straightforward and not resource-intensive. They can be lightning-fast, but they falter when the problem is complex.
What works: Smaller models can be a deliberate bet if you want less latency and can manage with accuracy going down by a few points. But this decision should be well-calculated. In one of our projects, when we switched from Sonnet 3.5 to Haiku, we gained 5 seconds in latency but lost 11 points in accuracy.
Using closed-weight, large parameter models can give you an accuracy baseline to begin with. Then move to the next best to check the drop in accuracy. Continue this process till you hit the mark beyond which you can’t compromise.
Alternatively, as token rates of open-weight models are increasing, you can use them for simple tasks to respond within a second.
Challenge 4: The more agentic your system looks, the slower it gets
In one of the agentic design patterns, the orchestrator identifies the intent and routes the task to the right agent. The design is clean and resilient but slow, as it includes multiple LLM calls. When you decide to optimize the number of LLM calls from 4 to 2, latency drops by 8-9 seconds. One call decides what needs to be done, and the other generates the final response. But this decision makes agents become less autonomous as the orchestrator decides everything beforehand. Accuracy drops as well. Deciding how much autonomy an AI agent needs is therefore an architectural choice, not simply a product feature.
What works: Instead of creating chains of tasks, work in parallel. Using the cheaper model for intent detection and routing and the costlier one for final generation, or vice versa, can also cut down the cost. For recurring intent and routing tasks, caching the decision to skip the classification step could be a time-saving decision.
The 2 LLM-call version pre-decides everything before execution. But if you give the second call access to a tool or agent instead of just generating a response, then it can still re-route mid-task if it feels the plan is not right. This recovers some agentic flexibility. Confidence-gated routing could be another solution. Run the 2 calls first; if the confidence is high, continue using it. If not, then move to 4 calls.
Challenge 5: AI can find the right problem but misjudge the fix
System audits (for accuracy, latency, security, etc.) using AI tools are picking up speed. These tools are great at identifying gaps, suggesting alternatives or fixes, and determining how much gain that step would ensure. Most importantly, these reviews are quick, and anyone can do it. In one of our projects, our customer used Claude for the same, and the system issues it identified were all spot-on.
The problem is these tools are not completely reliable. Directionally, these tools are often right. But the fixes they suggest and the projected gains they show are not always aligned and correct. They can identify the right lever only to misjudge what’s pulling them or to use the wrong metrics to calculate benefits. In our case, the tool suggested a latency drop by 5-6s when the gain was only 500 milliseconds.
What works: An expert’s opinion on the report generated by AI is crucial. If you trust AI blindly, it can lead to the wrong product roadmap creation. Make subject matter experts mandatory for any technical review of the audits. Also, critiquing prompts by using a competing LLM could be beneficial. Teams can use a second model or a predefined red-team process to challenge the audit’s recommendations, go after hallucinations, mathematical errors and misjudged system levers.
Conclusion
Multi-agent systems are a direction right now. The challenge with agentic AI isn’t simply getting agents to work, it’s determining which trade-offs the application can tolerate in production. But adoption and success depend not just on technology alone; settling the trade-offs before starting the development process is equally important.
Speed, cost and accuracy form a triangle. You don’t get all three. Plan for the best two and align them with your purpose. Human-in-the-loop is necessary, as you cannot bank on AI’s judgement alone. The product roadmap must include a base accuracy level, what things you can trade to keep your product running, and an expert who can intervene before any serious issue comes up.