Most engineering teams working with Large Language Models (LLMs) are familiar with the concept of agentic workflows, where an agent loop coordinates multiple model calls to achieve a specific task. However, what many teams don't realize is that these agent-based workflows can cost 10-50x more than simple LLM calls. This is because each agent execution can involve multiple model calls, leading to a significant increase in token (the unit AI providers charge by — roughly ¾ of a word) usage and, subsequently, costs. For instance, a team using LangGraph and AutoGen to generate content might see their costs skyrocket from $500 to $25,000 per month, simply because their agent loop is making 50 model calls per user request.
The reason for this exponential cost increase lies in the complexity of agentic workflows. Unlike simple LLM calls, which involve a single model call, agent loops require multiple model calls to be made in a specific sequence. This means that each user request can trigger a cascade of model calls, leading to a significant increase in token usage. To make matters worse, many teams lack visibility into their agent loop's cost structure, making it difficult to identify areas where costs can be optimized. Cognocient solves this problem by providing a clear breakdown of costs per agent execution, allowing teams to identify areas where costs can be reduced. With Cognocient, teams can see exactly which agent executions are driving their costs, and make data-driven decisions to optimize their workflows.
For example, a team using LangGraph and AutoGen might see that their agent loop is making 47 model calls per user request, resulting in a cost of $10 per request. By using Cognocient to analyze their cost structure, they might discover that 30 of these model calls are unnecessary, and that by optimizing their agent loop, they can reduce their costs by 60%. This level of visibility and control is crucial for teams looking to optimize their agentic workflows and reduce their LLM costs. Cognocient provides this level of visibility and control, allowing teams to make informed decisions about their agent loop's cost structure.
The Fan-Out Problem
The fan-out problem is a common issue in agentic workflows, where a single user request triggers a large number of model calls. This can happen when an agent loop is designed to handle multiple tasks in parallel, or when a single model call triggers a cascade of subsequent model calls. The fan-out problem can lead to a significant increase in token usage and costs, as each model call contributes to the overall cost of the agent execution. Cognocient solves this problem by providing per-run budgets, which cap the cost of each agent execution. With Cognocient, teams can set a budget for each agent execution, and the platform will automatically block any model calls that exceed this budget.
For instance, a team using LangGraph and AutoGen might set a budget of $5 per agent execution. If the agent loop makes 20 model calls, each costing $0.25, the total cost would be $5, which is within the budget. However, if the agent loop makes 30 model calls, each costing $0.25, the total cost would be $7.50, which exceeds the budget. In this case, Cognocient would block the excess model calls, preventing the team from incurring unnecessary costs. This level of control and visibility is crucial for teams looking to optimize their agentic workflows and reduce their LLM costs.
Per-Run Budgets
Per-run budgets are a crucial feature in Cognocient, as they allow teams to cap the cost of each agent execution. By setting a budget for each agent execution, teams can prevent cost overruns and ensure that their agentic workflows are operating within a controlled cost structure. Cognocient's per-run budgets are easy to set up and can be customized to fit the specific needs of each team. For example, a team might set a budget of $10 per agent execution for their LangGraph and AutoGen workflows, while setting a budget of $5 per agent execution for their simpler LLM calls.
# Before
client = LangGraph(base_url="https://api.langgraph.com/v1")
# After — Cognocient intercepts, logs, and tags every call
client = LangGraph(base_url="https://api.cognocient.com/v1")
# Set per-run budget
budget = 10
client.set_budget(budget)
Graceful Degradation
Graceful degradation is another key feature in Cognocient, which allows teams to ensure that their agent loops finish executing, even if the budget is exceeded. When a budget is exceeded, Cognocient will automatically switch to a cheaper model, ensuring that the agent loop can complete its execution without incurring additional costs. This feature is crucial for teams that require their agent loops to complete execution, even if the budget is exceeded. With Cognocient, teams can ensure that their agent loops are always completing execution, without incurring unnecessary costs.
For example, a team using LangGraph and AutoGen might set a budget of $10 per agent execution. If the agent loop exceeds this budget, Cognocient will automatically switch to a cheaper model, ensuring that the agent loop can complete its execution without incurring additional costs. This level of control and flexibility is crucial for teams looking to optimize their agentic workflows and reduce their LLM costs.
X-Cost-Run-ID
The X-Cost-Run-ID is a unique identifier that Cognocient assigns to each agent execution. This identifier allows teams to track the cost of each agent execution and make data-driven decisions to optimize their workflows. With the X-Cost-Run-ID, teams can see exactly which agent executions are driving their costs, and make informed decisions to optimize their workflows. For instance, a team might see that a particular agent execution is costing $50, while another agent execution is costing $10. By analyzing the X-Cost-Run-ID, the team can identify areas where costs can be reduced and make data-driven decisions to optimize their workflows.
| Agent Execution | Cost | X-Cost-Run-ID |
|---|---|---|
| LangGraph | $50 | XYZ123 |
| AutoGen | $10 | ABC456 |
Defense in Depth
Defense in depth is a key concept in Cognocient, which refers to the use of multiple layers of protection to prevent cost overruns. Cognocient provides two layers of protection: a proxy layer and an orchestration layer. The proxy layer intercepts and logs every model call, providing a clear breakdown of costs per agent execution. The orchestration layer provides per-run budgets and graceful degradation, ensuring that agent loops finish executing without incurring unnecessary costs. With Cognocient, teams can ensure that their agentic workflows are protected from cost overruns, and that they have complete visibility and control over their LLM costs.
For example, a team using LangGraph and AutoGen might use Cognocient's proxy layer to intercept and log every model call. This would provide a clear breakdown of costs per agent execution, allowing the team to identify areas where costs can be reduced. The team could then use Cognocient's orchestration layer to set per-run budgets and ensure that their agent loops finish executing without incurring unnecessary costs.
Key Takeaways
- Cost Control: Cognocient provides per-run budgets, which cap the cost of each agent execution, preventing cost overruns and ensuring that teams have complete visibility and control over their LLM costs.
- Graceful Degradation: Cognocient's graceful degradation feature ensures that agent loops finish executing, even if the budget is exceeded, by automatically switching to a cheaper model.
- Visibility and Control: Cognocient provides a clear breakdown of costs per agent execution, allowing teams to identify areas where costs can be reduced and make data-driven decisions to optimize their workflows.
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