The Cost of Autonomy: Optimizing Agent Token Usage

I had a painful realization last month: my agent was spending more tokens on unnecessary work than on actual problem-solving. When I dug into the costs, I found that 40% of our token budget was going to redundant retries, verbose explanations no one read, and exploration paths that led nowhere.

Token costs aren't just a financial concern—they're a constraint on what your agents can do. Here's how I've learned to think about and optimize token usage.

Where Tokens Actually Go

Before optimization, you need visibility. I break down token usage into categories:

• Productive reasoning: Actual problem-solving and decision-making
• Context management: Understanding and maintaining state
• Communication: Explaining decisions and output formatting
• Exploration: Trying approaches that don't work out
• Redundancy: Repeated work that adds no value

The last two categories are where most waste happens.

The Redundancy Problem

Common sources of token waste I've encountered:

Re-reading Context

Agents that re-read the same context on every step. Instead of maintaining a summary, they process the full history repeatedly. I've seen agents burn 10k tokens re-reading information they already knew.

Verbose Explanations

Agents that explain everything in detail when a brief summary would suffice. "I'm going to try X because of reasons A, B, and C" vs "Trying X."

Exploratory Dead Ends

Trying approaches that were obviously not going to work. Without good heuristics, agents explore every option equally, including the bad ones.

Optimization Strategies

Strategy 1: Context Summarization

Don't let context grow unbounded. Periodically summarize:

• What's been tried and what happened
• What's known about the current state
• What remains to be done

Replace detailed history with compressed summaries. The agent doesn't need to remember every token of the journey—just the relevant conclusions.

Strategy 2: Adaptive Verbosity

Not all situations need the same level of detail:

• Routine operations: Minimal logging
• Decision points: Moderate explanation
• Unexpected situations: Full detail

Calibrate verbosity to the stakes. You don't need a paragraph explaining why the agent chose the obvious option.

Strategy 3: Exploration Budgets

Limit tokens spent on exploration:

• Set a maximum percentage of budget for exploration
• Require confidence thresholds before exploring alternatives
• Cut off exploration that's not showing progress

Exploration is valuable, but it needs bounds.

Strategy 4: Caching and Reuse

Don't recompute what you already know:

• Cache the results of expensive operations
• Reuse reasoning across similar tasks
• Maintain working memory that persists

If the agent figured something out once, it shouldn't need to figure it out again.

The Quality-Cost Tradeoff

Optimization isn't about minimizing tokens at all costs. It's about finding the right tradeoff:

• Too few tokens: Agent doesn't have room to think, quality suffers
• Too many tokens: Wasted resources, slower execution
• Right balance: Enough tokens for quality, not so many that you're wasteful

I aim for "efficient effectiveness"—the minimum tokens needed for high-quality outcomes.

Budgeting Patterns

I use different budgeting patterns depending on the task:

Fixed Budget

For routine tasks with predictable costs. "This type of task typically takes 5k tokens, so budget 7k for safety."

Tiered Budget

For tasks of varying complexity. "Simple: 3k, Medium: 10k, Complex: 25k." Classify the task first, then allocate accordingly.

Dynamic Budget

For open-ended tasks. Start with a base budget, allocate more as needed, but with diminishing returns and hard caps.

Measuring and Monitoring

You can't optimize what you don't measure. I track:

• Tokens per task type: Which tasks are most expensive?
• Tokens per outcome: Does spending more improve results?
• Waste ratio: What percentage goes to redundancy?
• Efficiency trends: Is the agent getting more or less efficient over time?

When to Spend More

Not all token spending is bad. Worth spending more when:

• The task has high stakes
• Errors are costly to fix
• The problem is genuinely complex
• Quality matters more than cost

The goal isn't to minimize token usage—it's to maximize value per token.

Starting Your Optimization

If you're not currently tracking token usage:

• Week 1: Add basic token tracking
• Week 2: Categorize where tokens go
• Week 3: Identify the biggest waste sources
• Week 4: Implement targeted optimizations

Start with visibility. Optimization follows naturally once you can see the problem.

How are you managing token costs in your agent systems? I'm always looking for better optimization techniques. Find me at matt@emmons.club.