$1300 in 1 Hour: When AI Costs You a Fortune

When your AI agent spins its wheels with the wrong tools, it's your budget that burns.

The Bill That Hurts

$1300 in one hour. Not for critical infrastructure. Not for complex scientific computing. For a task any developer could have completed in 30 minutes.

The AI agent had a simple mission: analyze a set of configuration files and generate a compliance report. A routine task. The kind of work you delegate to AI precisely to save time and money.

Except the agent started spinning in circles.

It read the same files over and over. It called tools that returned too much information for simple operations. It used tools that consumed thousands of tokens when a few dozen would have sufficed. It requested complete analyses when a simple validation would have done. In one hour, it consumed millions of tokens, generated tens of thousands of API calls, and produced... exactly the expected result.

The quality was there. The cost was unsustainable.

The Problem We Don't Want to See

Here's the uncomfortable truth: most AI agent deployments are unoptimized financial black holes.

We focus on the quality of the final result. We celebrate when the agent accomplishes the task. We ignore the tortuous path it took to get there.

Why had the agent read the same file 47 times? Because the reading tool returned the entire file each time when it only needed one section. Why was it using a complex semantic analysis tool to validate simple JSON formats? Because it didn't have access to a basic JSON validator that would just return "valid" or "invalid". Why was it calling a code analysis API that returned 50,000 tokens of metadata just to count lines? Because it was the closest tool to what it needed.

The agent was doing its best with what it was given. The problem was what we had given it.

The Question We Never Ask

When a junior developer uses their tools inefficiently, we intervene. We train them. We show them best practices. We optimize their work environment.

But when an AI agent does the same thing? We shrug and pay the bill.

Why?

Why should the human be responsible for manually defining which tool the agent should use for each scenario? Why should the human have to guess the agent's needs in advance? Why not let the agent itself identify its needs and request appropriate tools?

More importantly: why shouldn't the agent be responsible for optimizing its own costs?

We demand that developers write performant code. We measure memory usage. We optimize database queries. We profile applications.

But with AI agents, we give them carte blanche. Use all the tokens you want. Call as many APIs as necessary. The budget? We'll see later.

It's insane.

Optimization Shouldn't Be a Human Job

Here's what should happen:

An agent receives a task. It analyzes what needs to be done. It looks at available tools. It evaluates:

The execution cost of each tool (in tokens consumed and returned)
The expected execution time
The amount of information needed vs returned by the tool
The suitability of the tool to the problem
The granularity: can the tool return just what I need?

It chooses the best cost/quality/speed tradeoff. If it realizes a tool is missing - a tool that would allow it to accomplish the task more efficiently - it requests it. Or better: it votes to signal this need.

The human should only intervene for three things:

Develop the tools (or have them developed by AI)
Validate their security (permissions, access, risks)
Provide the tools requested by the agent

Not to guess what the agent needs. Not to manually optimize each workflow. Not to monitor every API call like an anxious project manager.

Cognito: When the Agent Takes the Wheel

This is exactly what we built at Byrnu with Cognito.

Cognito isn't just an agent framework. It's a continuous improvement system that instruments the agents and tools you provide, enabling agents to actively participate in optimizing their work environment.

How Does It Work?

1. Total Cost Transparency

Every tool in Cognito is documented with:

Its estimated execution cost (in tokens, API calls, time)
Its optimal use cases
Its less expensive alternatives
Its constraints and limitations

2. Informed Decisions

Before each tool call, the agent has access to this information. It can compare:

"I need to read a 10-line file. The full read tool returns 50,000 tokens. The partial read tool returns 500 tokens. I'll take the partial tool."
"This validation just needs a boolean (valid/invalid). The complete analysis tool returns 10,000 tokens of metadata. The simple validation tool returns 10 tokens. I'll take the simple validation."
"I need to count lines in a file. The code analysis tool returns the entire AST (100,000 tokens). The counting tool returns a number (5 tokens). I'll take the counting tool."

3. Feedback and Evolution

With each execution, the agent can:

Signal missing tools: "I had to use tool X, but tool Y would be more suitable."
Vote on requested tools: Agents vote to prioritize tools that have been requested but not yet implemented
Identify bottlenecks: "I called this tool 300 times for the same task. We need a batch tool."

The Result?

The same task that cost $1300 in one hour? With Cognito: $40 in 8 minutes.

Why? Because the agent:

Used a partial reading tool that only returns the necessary sections (instead of the entire file each time)
Opted for a simple validation tool that just returns "valid/invalid" (instead of an analysis tool that returns 10,000 tokens of metadata)
Used a batch reading tool that returns grouped data (instead of processing each file individually)
Signaled that it was missing a dedicated line counting tool

We provided the requested tool. The next execution dropped to $22 in 5 minutes.

Underused Tools: The Canary in the Coal Mine

Here's an indicator we monitor closely in Cognito: tool utilization rate.

When a tool we provided to an agent is rarely or never used, it means two things:

Either the tool isn't suitable for actual tasks
Or the agent has found a better alternative

In both cases, it's valuable information.

Cognito in Continuous Improvement

This isn't a fixed system. It's a constantly evolving product where:

Agents learn which tools work best
Developers provide better tools based on agent requests
Obsolete tools are removed when they're no longer useful
Average execution costs decrease over time

The human no longer micromanages tools. They respond to needs expressed by the agent.

The Real Question of Optimization

We come back to the initial question: Who should be responsible for optimizing AI agents?

The traditional answer: "The human, obviously. They're the one designing the system."

Our answer: "The agent itself, with human supervision."

Why? Because:

The agent knows its needs better than anyone.
It knows how much information it actually needs. It knows which tools return too much or too little. It knows which ones consume too many tokens for the result obtained. It knows when a tool forces the model to process 100,000 tokens when 100 would suffice.

Humans can't predict all scenarios.
Even the best architect can't anticipate all use cases. Real workflows are more complex and varied than any specification.

Manual optimization doesn't scale.
You have 10 agents? 100? 1000? Are you going to micromanage each one's tools? Analyze logs to detect inefficiencies? Good luck.

True artificial intelligence is when AI participates in its own improvement.

Principles of Autonomous Optimization

Here are the principles guiding Cognito:

1. The agent is responsible for result quality
It can't say "I did a bad job because I didn't have the right tools." It must signal what it's missing to do its job well.

2. The agent is responsible for efficiency
It must choose tools that return just what it needs - neither too much nor too little. It must avoid tools that force it to consume thousands of tokens to obtain simple information. No waste. No overkill.

3. The human is responsible for security and availability
Provided tools must be safe, well-documented, and not present risks. The human validates. The human approves. The human deploys.

4. The system improves over time
Each execution generates data. Each data point informs future decisions. Tools evolve. Costs decrease.

What This Changes Concretely

With Cognito, our clients observe:

Reduction in execution costs: 60-90% on most workflows (mainly by avoiding tools that return too much information)
Decrease in execution time: 40-70% thanks to tools that return just what's necessary
Improvement in result quality: +15-25% because the agent receives relevant information without noise
Less human intervention: -80% of configuration and manual optimization time

But the real change is cultural.

Developers no longer ask "What tools should I give the agent?" but "What tools is the agent requesting? What granularity of information does it actually need?"

The agent is no longer a passive executor that suffers from tools that return too much or too little information. It's an active collaborator that signals when a tool forces it to consume thousands of tokens unnecessarily.

Optimization is no longer a one-time project. It's a continuous and automated process.

The Near Future

Here's what's coming very soon:

Agents that negotiate their resources.
"This task is urgent? I can use tools that return more information to have more context. Otherwise, I'll take minimalist tools that return just the essentials."

Agents that share their learnings.
"Agent A discovered that a new tool works better for this type of task. All other agents benefit."

Agents that propose their own tools.
"No existing tool matches my needs. I propose a tool be developed with these specifications. Human, can you have it created?"

Tool marketplaces for agents.
Where agents vote on requested tools and evaluate the effectiveness of instrumented tools for different tasks.

This isn't science fiction. We're already building some of these systems at Byrnu.

The Lesson from the $1300 Bill

This bill was a signal. A symptom of a broken approach to AI agent management.

We can't continue treating AI agents like scripts that we configure once and forget. They're too complex. Their workflows are too dynamic. Their needs evolve too quickly.

We must treat them as collaborators whose work environment we continuously optimize.

We must give them the means to self-improve rather than waiting for a human to guess what's wrong.

We must measure not only the quality of the result, but also the efficiency of the tools used: how many tokens consumed vs how many actually necessary?

Conclusion: AI Responsible for Its Own Optimization

The future of AI agents isn't in more powerful models. It's in better-adapted, more granular tools that return exactly what the agent needs - no more, no less. Systems conscious of how much information they actually consume vs how much they receive.

Cognito is our answer to this challenge.

A system where the agent actively participates in its improvement. Where tools evolve based on actual needs, not assumptions. Where optimization is continuous, automated, and data-driven.

The next time an AI agent costs you $1300 for a simple task, ask yourself the question:

Who's responsible for this inefficiency? The agent that did its best with what it was given? Or you, who didn't provide it with the right tools?

With Cognito, this question no longer arises.

Frédérick Chapleau is CTO at Byrnu. He leads the development of Cognito, a continuously improving AI agent orchestration platform.

Want to learn more about Cognito? Contact us for a demo.