20,000 Tokens for "Hello"? An AI Agent's Excessive Reasoning Is a Deliberate Trap

"If you cannot control a structure where 20,000 tokens melt away on a greeting and 40,000 on writing code, you are left with nothing but a deficit instead of a productivity gain."

Watching the "excessive reasoning" process that has become the recent trend among agents, one cannot shake the suspicion that this is not a mere technical limitation but a thoroughly deliberate structure.

1. A Simple Greeting, "Hello," Melts a Whole Large-Model Prompt

A Single "Hello" Evaporates 20,000 Tokens by Default

The moment it is connected to an agent system, the AI starts reasoning (Thinking), spinning up every scenario on its own — is this greeting a signal to run a skill, an instruction to update an Obsidian note, or a hint to check important mail?

The skill list of over 70 items (system prompt), past conversation history, and even the embeddings of linked data — all of it hangs off that one short greeting.

Actual token breakdown of a single "Hello":

System prompt (agent rules + skill list):   8,000-12,000 tokens
Past conversation context (last 5 turns):   3,000-6,000 tokens
Tool schema (70 skill definitions):         5,000-8,000 tokens
Reasoning process (internal monologue):     2,000-5,000 tokens
Output ("Hello! How can I help?"):          50-100 tokens
──────────────────────────────────────────────
Total: 18,000-31,000 tokens (about 20,000 on average)

Cost conversion (by model):

Say "hello" to Sol or Opus and it costs about 100 won. Greet 50 times a day and it is $5.15 = about 6,700 won. On greetings alone.

The Chilling Ratio of Reasoning Tokens

In a typical agent response, the token ratio of each component:

[Reasoning / internal monologue]  ████████████████████  40-60%
[System prompt]                    ████████            20-30%
[Tool schema]                      ████               10-15%
[Actual output]                    █                  2-5%

The actually useful output is only 2-5% of the total tokens. The rest is the agent's internal monologue, talking to itself.

2. Write One Bit of Claude Code and 40,000 Tokens Vanish

Modifying or Generating Code Consumes 40,000 Tokens by Default

You only asked to fix or add a single line of code, yet the agent, in the name of preventing errors, runs an internal monologue and a self-Q&A loop like crazy.

Token breakdown of a one-line code edit (based on Claude Code):

System prompt:                     3,000 tokens
Project context (file structure):  2,000 tokens
Entire target file content:        8,000-15,000 tokens
Related file references (imports): 3,000-5,000 tokens
Reasoning ("if I change this part like this..."): 5,000-10,000 tokens
Output (the modified code):        200-500 tokens
──────────────────────────────────────────
Total: 21,200-40,500 tokens

Cost of "emitting one line of output":

Thirty code edits a day is $2.13 = about 2,800 won on Sonnet. A week is about 20,000 won.

The Reality of an Agent's "Thinking Process"

What happens internally when an agent edits code:

Step 1: "The user told me to edit line 42 of this file"
Step 2: "Which other functions does the function on this line call?"
Step 3: "Which other files are affected by the change?"
Step 4: "I need to verify whether this approach is safe"
Step 5: "Let me compare with the previous version"
Step 6: "Let me check again for typos"
Step 7: "Does this code style match the project rules?"
Step 8: "Write the output"

Through 8 steps, it fixed 3 lines of a 200-line file. The other 7 steps are all tokens.

3. Excessive Reasoning: Technical Shortfall or Deliberate Design?

Watch quietly, and you see agents go through an excessively long and verbose reasoning process even for obvious questions or trivial tasks that need no deep thought.

What the user feels vs the actual cost:

Why This Structure Persists

From the platform and API vendor's standpoint, the more tokens an agent consumes, the more profit is left over.

Agent token consumption ↑ → API revenue ↑ → platform profit ↑
         ↓
  User perceives it as "smart"
         ↓
  Encourages more usage

The "nice-sounding agent automation environment" is, in effect, a lawful token-extraction structure.

Excessive Reasoning Proven with Real Data

Same task, reasoning-token ratio by model:

Sol's reasoning-token ratio is 3x DeepSeek's, assuming the same task.

4. Five Holes Where Money Leaks Through Reasoning Tokens

Hole 1: Repeated Resending of the System Prompt

On every API call, the system prompt (agent rules, skill list, and so on) is resent in full. Call a 10,000-token system prompt 50 times and 500,000 tokens evaporate on the system prompt alone.

Hole 2: Accumulation of Tool Schemas

The API schemas of 70 skills are included on every call. At 100-200 tokens per schema, 7,000-14,000 tokens are attached every time.

Hole 3: The Snowball of the Context Window

The longer the conversation, the more the whole context is resent every turn.

Turn 1: 5,000 tokens → $0.015
Turn 5: 25,000 tokens → $0.075
Turn 10: 60,000 tokens → $0.180
Turn 20: 150,000 tokens → $0.450
Turn 50: 500,000 tokens → $1.500

The difference between turn 1 and turn 50 is 100x, assuming the same amount of work.

Hole 4: Failure and Retry Spiral

When an agent calls the wrong tool it fails and retries upward to a stronger model. In this process, tokens swell 2-3x.

Hole 5: Shadow Tokens

The system consumes tokens where you cannot see:

• Response-formatting metadata
• A copy for safety-filter validation
• Token storage for logging
• API wrapper overhead

10-40% of the visible tokens are billed extra as shadows.

5. A Practical Checklist to Stop the Cost

Do This Right Now

[ ] Set a daily limit of $5 or less in the OpenAI/Anthropic dashboard
[ ] Compress the agent system prompt to under 3,000 tokens
[ ] Disable unused skills/tools
[ ] Include "answer concisely" in the system prompt
[ ] Limit the context window to under 8K (for simple tasks)

Cut Cost 10x with a Routing Strategy

Hand everything to Opus and it is $375 a month; route it and it drops to $30-50 a month.

Conclusion: Wake Up from the Sweet Fantasy and Build the Cost Wall First

Fall for the sweet marketing that "an agent handles your mail and knowledge management for you," plug in an API key, and use it recklessly, and you may go bankrupt on a bill shock.

If you cannot control a structure where 20,000 tokens melt away on a greeting and 40,000 on writing code, you are left with nothing but a deficit instead of a productivity gain.

An agent without a cost-control mechanism is not a smart assistant but the most refined thief, lawfully plundering your money.

Stop your agent experiments right now, or set the hard limit in the dashboard very low.

Previous post: The Truth About AI Agent Token Costs — The Science of How 70 Skills Pick Your Wallet

Sources: Spheron Network, AgentMarketCap, TokenFence, Augment Code, AIMadeTools (as of August 2026)