10 Tips to Improve AI Agent Token Efficiency

Rising generative AI production costs are constraining business adoption despite growing investment in the technology, according to a June 2024 Lucidworks survey  of 2,500 business leaders. Gartner estimated that over 40% of Agentic AI projects will be canceled by end of 2027 due to escalating costs, unclear business value or inadequate risk control [link ].

Token cost is the primary driver. Tokens are the new electricity and efficiency matters beyond just the bill. Every token consumed draws on real compute resources, and that has a planetary cost worth taking seriously.

This blog distills 10 practical techniques to improve token efficiency and drive down cost.

LLM cost structure looks quite different depending on whether you're using a self-hosted infrastructure or managed services.

Managed Services (e.g., Amazon Bedrock, OpenAI, Anthropic):

Because agents call the LLM iteratively in internal loops, the cost compounds across three variables:

To improve token efficiency, we have 3 levers: 1) reduce agent recursion, 2) lower per request token count, and 3) use lower cost models when possible.  

The rest of this article focuses on managed services, since this is the most common deployment pattern, and where you have the most room to optimize.

Agent recursion is the number of times your agent calls the LLM within a single invocation session. Because agents are non-deterministic, there's no way to predict exactly how many recursive calls a given request will trigger. The best starting point to get a baseline estimate is to run through representative scenarios and collect empirical recursion counts. To prevent runaway costs, the standard practice is to set a recursion limit based on the estimates. Techniques to reduce agent recursion include:

Vague instructions force the agent to spend extra turns interpreting what it's supposed to do. Compare these two:

The clearer version leaves no room for the model to wander. It knows exactly what done looks like.

Although the clear prompt is often a little longer, these extra tokens are well spent. The iterations saved are worth way more than a few additional tokens upfront.

Multi-agent graphs are intuitive to design, but every node you add has a cost. OpenAI’s guidance on this is ‘Start with one agent whenever you can. Add specialists only when they materially improve capability isolation, policy isolation, prompt clarity, or trace legibility. Splitting too early creates more prompts, more traces, and more approval surfaces without necessarily making the workflow better.’[link ]

When designing a multi-agent graph, interrogate each proposed sub-agent node: does it genuinely meet the bar for being its own agent, or is it just adding overhead?

More capable models tend to reason more efficiently and reach the goal in fewer steps, as a result the higher per-token cost can actually pay for itself [link ]. This applies to the use cases where it needs strong reasoning capabilities.

Note that this is a deliberate trade-off. We'll revisit model selection from a cost-per-token angle in tip 10.

 

Token count per LLM call is hard to control too. You can estimate your base system prompt size and cap user input at the application layer, but token count across recursions is tricky, because agents carry forward outputs from previous steps, and any tool calls or RAG retrievals get appended to the context as well. Here are 6 techniques to address this.

Prompt caching represents one of the most effective cost reduction strategies for generative AI agents. Amazon Bedrock's prompt caching capability can deliver up to 90% cost reduction. It is common for agents to have long instruction prompts, often much longer than user input. The autonomous nature of agents requires it to hit the foundation models recursively, combining previous thinking steps, results and conversation history. This increases token consumption. Prompt caching stores frequently reused prompt components—like system instructions and tool schemas. For agents that need a high number of recursions, the conversation history and repeated thinking steps can also be cached. Instead of retransmitting these static elements with every LLM invocation, prompt caching dramatically reduces billable tokens while maintaining full functionality.

Please see here   for a list of Bedrock models that support prompt caching.

Context isolation is a powerful but often overlooked cost lever. When a single agent handles an entire task end-to-end, context accumulates rapidly. Previous steps, tool outputs, and reasoning traces all pile into the same window. Most teams think of context management as an accuracy technique, but when set up correctly, it directly reduces token consumption too.

Sub-agents can be wired into your system in two ways: as a node in the agent graph, or as a tool. The distinction matters for cost. When a sub-agent is a graph node, its recursive traces can propagate into the parent agent's state, inflating the shared context window. When set up as a tool instead, the sub-agent executes its task in isolation and returns only a distilled result to the supervisor without leaking its intermediate traces upward. The orchestrating agent's context stays lean, and you only pay for the summary, not the full reasoning trace.

This pattern is especially effective for tasks involving parallel exploration such as deep research agents where each sub-agent can do deep work independently and return a compact output.

RAG systems often retrieve excessive context that inflates token consumption. Context pruning   can reduce costs by filtering retrieved content before sending it to the LLM. This technique analyzes semantic relevance of each chunk against the user's query and removes irrelevant portions. Effective pruning can reduce context size while maintaining answer quality, creating proportional cost savings.

User input control is effective when you have a user-facing interface. Enforcing a character or token cap at the application layer controls one of the few input variables you can control. This is distinct from the model's max input token setting. Max input in model config is intentionally high to accommodate system prompts, tool outputs, and iteration history. The application-layer cap is about preventing unnecessarily verbose user inputs from compounding costs across recursions.

Agents understand their available tools through tool definitions. Verbose definitions consume tokens unnecessarily. Keeping them accurate and tight reduces per-call token overhead and can also improve tool selection accuracy.

When a tool returns results directly to the agent, there's a risk of large, noisy payloads inflating the context. If you're using AgentCore Gateway, you can use its interceptor function to inspect and clean tool responses before they're returned to the agent by stripping unnecessary fields, truncating verbose outputs, or reformatting for token efficiency. If you're not using a gateway, similar cleaning logic can be applied in your tool wrapper layer, or within agent loop.

 

Unit token charge is fixed by the provider, but you can control which model you use. Not every task needs the most powerful one.

Amazon Bedrock offers over 100 LLMs, and websites such as artificialanalysis.ai  and llm-stats.com  provide detailed benchmarks to help you compare them and find the model that fits your task.

For example, if a sub-agent's job is primarily to select the right MCP tool and invoke it with inputs from the previous agent, it needs strong tool-calling accuracy, not deep reasoning. Llama 3.1 405B is a strong fit for that [benchmark link] and costs $2.40 per million input or output tokens on Bedrock, compared to Claude Opus 4.7 at $5 per million input tokens and $25 per million output tokens. That's a significant difference at scale.

We've been promised cheaper tokens, but efficiency can't wait for that day. And when cheaper tokens do arrive, demand will scale. The teams that have built efficiency into their architecture will simply do more with less.

Try one or two of these techniques on your next agent and drop a comment sharing how much you saved. I'd love to see what you find.