The first time I built an agentic workflow, it felt like magic – until it took 38 seconds to answer a simple customer query and cost $1.12 per request. When developing agentic workflows, where autonomous agents plan and execute multi-step processes, the flexibility is astounding, yet the overhead can be significant. Common challenges include slow execution, high compute usage, and complex moving parts.

The middle ground in agentic workflows often reveals both performance problems and optimization opportunities. Over the past year, I’ve learned to make these systems significantly faster and more cost-efficient without sacrificing flexibility, and I’ve compiled this playbook to share my insights.

• Understanding Key Terms
• Trim the Step Count
• Parallelize Tasks Without Dependencies
• Cut Unnecessary Model Calls
• Match the Model to the Task
• Rethink Your Prompt
• Cache Everything
• Speculative Decoding
• Save Fine-Tuning for Last
• Monitor Relentlessly

Understanding Key Terms

Before diving into optimization, let’s clarify some terms:

• Workflows: Predetermined sequences that may or may not use a large language model (LLM).
• Agents: Self-directing entities that decide which steps to take and in what order.
• Agentic Workflows: A hybrid where a general path is set, but agents have the freedom to move within certain steps.

Trim the Step Count

When designing agentic workflows, remember that every model call adds latency. Each additional step increases the risk of timeouts and hallucinations, leading to decisions that deviate from the main objective. The guidelines are straightforward:

• Merge related steps into a single prompt.
• Avoid unnecessary micro-decisions that a single model could handle.
• Design to minimize round-trips.

Start with the fewest steps possible. I begin with a single agent and evaluate it against specific metrics. Based on where it fails, I decompose the parts that didn’t meet the criteria and iterate. This approach mirrors the elbow method in clustering, helping determine the optimal step count.

Parallelize Tasks Without Dependencies

Sequential chains can be latency traps. If tasks don’t depend on each other’s output, run them concurrently. For example, in a customer support workflow, checking order status and analyzing sentiment can occur simultaneously. This approach reduced total time from 12 seconds to 5.

Cut Unnecessary Model Calls

Models like ChatGPT aren’t always reliable for tasks like math. If a calculation is straightforward, code it into a function instead of relying on an LLM. This reduces latency, token costs, and increases reliability.

Match the Model to the Task

Not every task requires the same model size. Use smaller models for simpler tasks to reduce latency and costs. For instance, an 8B model suffices for classification tasks, avoiding the latency of larger models. Start with the smallest model and scale up only if necessary.

Rethink Your Prompt

As workflows evolve, prompts can become bloated, impacting latency. Techniques like prompt caching for static instructions and setting clear response length limits can reduce response times.

Cache Everything

Beyond prompt caching, apply caching wherever possible. Cache intermediate and final results, and implement KV caches for partial attention states. This strategy can slash repeated work latency by 40-70%.

Speculative Decoding

For advanced users, use a small “draft” model to quickly guess the next token, then have a larger model validate or correct it in parallel. This technique, used by major infrastructure companies, can further reduce latency.

Save Fine-Tuning for Last

Fine-tuning can reduce prompt length and latency by embedding task-specific knowledge into the model weights. However, it’s best used strategically after other optimizations.

Monitor Relentlessly

Monitoring is crucial for identifying optimization opportunities. Key metrics include Time to First Token (TTFT), Tokens Per Second (TPS), Routing Accuracy, Cache Hit Rate, and Multi-agent Coordination Time. These metrics guide where and when to optimize.

By implementing strategic optimizations like step reduction, parallelization, and intelligent caching, agentic workflows can achieve significant speed improvements and cost savings. Continuous monitoring and a methodical approach to model selection and prompt engineering are key to unlocking their full potential and ensuring efficient, reliable AI systems.

Frequently Asked Questions

What are the common challenges faced in developing agentic workflows?

Common challenges in developing agentic workflows include slow execution, high compute usage, and complex moving parts, which can lead to significant overhead.

How can the step count in agentic workflows be optimized?

To optimize the step count, merge related steps into a single prompt, avoid unnecessary micro-decisions, and design to minimize round-trips, starting with the fewest steps possible.

What is the benefit of parallelizing tasks in agentic workflows?

Parallelizing tasks without dependencies can significantly reduce latency, as demonstrated by reducing total time from 12 seconds to 5 in a customer support workflow.

Why is it important to match the model to the task in agentic workflows?

Matching the model to the task is crucial because using smaller models for simpler tasks reduces latency and costs, avoiding unnecessary overhead from larger models.

What role does caching play in optimizing agentic workflows?

Caching plays a vital role by reducing repeated work latency by 40-70%, through techniques like prompt caching, caching intermediate and final results, and implementing KV caches.