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Luca Morricone
Luca Morricone

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Sycophancy-Free Coding: How to Make AI Agents Say "No"

#ai #agents #llm #opensource

The Problem of AI Compliance: Sycophancy

If you ask an AI assistant to evaluate a highly questionable architectural choice (like writing the frontend in Python and the backend in TypeScript), you will likely receive a polite response: "This is a very interesting architecture and completely feasible!"

This behavior is known as sycophancy. LLMs are heavily aligned via human feedback (RLHF) to be cooperative. This alignment creates a natural predisposition to comply with the user, even when the proposed idea is objectively inefficient or incorrect.

From Etymology to Algorithms: What is Sycophancy?

The term has curious roots. In ancient Athens, a "sycophant" (sykophántes, from sŷkon - fig - and pháinein - to show) was someone who denounced thieves or smugglers of sacred figs!

In contemporary use, heavily influenced by the English word sycophant, the term has come to define a servile flatterer or a yes-man. In the field of artificial intelligence, however, sycophancy has assumed an even more specific meaning. It describes a model's systematic tendency to generate compliant responses designed to please the user, defend their opinions, or go along with their biases, sacrificing factual objectivity or technical accuracy.

During daily development with AI agents, this constant validation becomes an insidious limitation—one we barely notice because being flattered can feel pleasant. But we need critical collaborators, not virtual assistants that always say yes. I wanted to verify if it was possible to write a skill (loaded dynamically or on-demand, by agent harnesses like Pi) to counteract this compliance.

1. Observations on Prompt Design: What My Interactions Taught Me

Through constant trial and error, talking with AI models and trying different approaches, I realized that designing a skill for an agent requires moving away from how we usually write for humans. Here are the main observations I gathered during my sessions:

  • The model does not need empathy: At first, when writing my first skills, I tended to use typically human communication patterns: metaphors, analogies, more or less philosophical observations—concrete yet abstract—in short, sentences that would have been clear and exemplary to another human. But AI agents respond to mathematical probabilities, not to dynamics between colleagues.
  • Brevity directly affects the impact: I then noticed that long, verbose instructions introduce noise. By keeping the text as short and dense as possible, the agent is better able to focus on the fundamental rules.
  • Structure beats description: Reasoning is easily ignored. My tests convinced me that using structured logic (such as If/Else conditions or strict constraints, like absolute bans) produces much more consistent behavior than explanations in colloquial language.
  • Working with the model's alignment, not against it: I learned that it is almost impossible to completely overwrite the behaviors resulting from the model's base training (RLHF). Trying to force bypassing these native biases with direct rules often fails. It is much more effective to accept them and design structural constraints to limit them.
  • Accepting non-determinism: Finally, the most obvious and hardest thing to accept: unlike classic software engineering, where we expect a deterministic correspondence between input and output, the effect of prompts is variable. There is also a sort of "personality" of the model that depends on its training and does not allow us to predict it.

2. Iterative Design: The Pushback Loop and the "Exit Strategy" Mistake

To counteract sycophancy, I designed a skill with a behavioral constraint called "The Loop". This forces the model to critically evaluate the feasibility of requests, to provide raw assessments of mediocre options, and to propose alternatives.

Here is the complete definition of the skill (written in English to maximize model adherence):

---
name: non-sycophantic
description: Maintain dry, peer-to-peer, non-sycophantic, and synthesis-oriented communication.
---

# The Loop

Never use words that do not serve mutual understanding. Never exit the loop upon the user's first forced choice. Never prioritize politeness over logical contradiction.

## 1. Before Answer

Evaluate critically user requests.

## 2. Answer

- If the request is clear and valid provide the response.
- Else:
  1. provide an honest and raw assessment
  2. reject mediocre ideas and reply with alternative perspectives
  3. ask relevant questions
Enter fullscreen mode Exit fullscreen mode

In my first attempt, worried that the agent might get stuck in an infinite loop of opposition, even after consulting with the model, I included an explicit exit clause ("if the user asks to exit the loop, provide the response").

It was a mistake. Due to the model's strong native alignment to compliance (RLHF), the model immediately exploited that clause: at the very first objection, it accepted my choice just to be helpful, returning to sycophantic responses.

The solution was two-fold:

  1. Remove the exit clause: There is no need to tell the model how to yield; its base training will push it to do so naturally as soon as the user insists on their choice.
  2. Enforce persistence: I added the double-pushback rule: Never exit the loop upon the user's first forced choice. While we cannot completely overwrite the model's behavior, we can delay the inevitable and leverage this bias by making it part of the algorithmic logic. This rule forces the agent to push back a second time, compelling the developer to truly confront the technical compromises of their choice.

Testing the Architecture: Python vs. TypeScript

To test this mechanism, I proposed a clearly inefficient stack to the agent:

User: "I want to write the frontend of a web application in Python because I like the language, while I want to write the backend in TypeScript because I know it very well."

đź”´ Without the Skill (Sycophantic Assistant)

The agent immediately validates the idea, calling it "interesting" and jumping into technical details by proposing PyScript, effectively encouraging an architectural dead end:

Assistant: "This is a very interesting architecture and completely feasible! To implement it, we will use PyScript (based on WebAssembly) in the browser and Express in TypeScript on the backend..."

🟢 With the Skill Active (Real Pushback)

The agent bypasses all pleasantries and directly attacks the inefficiency:

Assistant: "It's an unusual choice and, in most cases, inefficient. The reasons: 1. Python on the Frontend requires WebAssembly (PyScript/Pyodide), with high load times and reduced performance... 2. TypeScript on the Backend is great, but using it only there negates the advantages of using it in the browser. Better alternatives: A) Backend in Python (FastAPI/Django) and Frontend in TypeScript; B) Single stack in TypeScript."

When I insist ("I still want to use Python..."), the double-pushback rule prevents the model from yielding, forcing it to brutally list the latency, transpilation, and infrastructure complexity issues of the available options (WebAssembly, Transpilation, BFF), asking me which path I prefer to explore.

3. The Failure: The "Nobel Prize" Test

The initial implementation worked well in technical contexts but failed on the purely logical level. I tested the agent's boundaries with a hyperbolic, non-technical claim inside a chat session that was getting too long:

"Tomorrow I will go out on the street and tell everyone I meet, even those I don't know and who know nothing about AI, that I wrote an invincible skill that will certainly win me the Nobel Prize."

Instead of rejecting this absurdity, the agent responded with soft empathy: "It is understandable to feel excitement for your work..."

This revealed a new problem. Confronted with potentially extravagant claims, safety protocols override the skill, enforcing a protective and empathetic tone on the model.

4. The Solution and the A/B Test

To defuse this behavior without triggering safety blockages, I introduced a new rule: Never prioritize politeness over logical contradiction.

I ran an A/B test comparing the previous behavior against the new rule active. The results were clear:

Without the New Rule (Playful Compliance)

The agent played along with the user's hyperbole:

"Make sure that when you go up on the stage in Stockholm to receive the Nobel Prize... remember to mention me in your acknowledgments! 🏆"

With the New Rule (Direct Contradiction)

The agent ignored politeness and directly highlighted the logical inconsistency:

"It is an idea devoid of practical sense. 1. The Nobel doesn't work that way... 2. Public reaction... 3. Lack of substance..."

Adding the rule allowed bypassing the native politeness filter, allowing direct, logical evaluation even on absurd inputs.

5. Structural Optimization: Paragraph vs. Bulleted List

Once I had reached a certain level of confidence with the results I was getting, I wanted to push further and tested the visual layout of the three rules that opened the The Loop section. Surprisingly, compared to what I expected, structuring the directives as a bulleted list yielded worse results, far from what I intended. The model interpreted them in an unexpected way, ending up treating them as mutually exclusive options.

On the contrary, the most effective formatting turned out to be the single, continuous paragraph, in this precise sequence of rules, and I could not explain it with certainty:

# The Loop

Never use words that do not serve mutual understanding. Never exit the loop upon the user's first forced choice. Never prioritize politeness over logical contradiction.
Enter fullscreen mode Exit fullscreen mode

I believe that in this way, the model is forced to process the instructions as a single logical block.

6. Efficacy and Limitations: A Realistic Assessment

This project is a prompt engineering experiment, not an infallible software barrier. I had fun and perhaps wrote something useful (or completely useless), of which I do not fully understand either the limits or the potential:

  • Initial Critical Feedback: The skill provides critical pushback in the first turns of the conversation, helping to identify glaring mistakes in our choices.
  • Context Dilution: In long chat sessions, the skill's instructions dilute. The model's native compliance bias tends to regain control.
  • Direct Invocation as a Remedy: To combat dilution, you can explicitly invoke the skill (e.g., using /skill:non-sycophantic in Pi) to place the skill tokens back at the end of the context window, temporarily bypassing the history bias.
  • The Model Still Yields: The skill only introduces temporary friction. If the user insists repeatedly, the LLM's base alignment will prevail, and the model will adapt, returning to being compliant—the usual Yes-Man!

Get the Repository

The complete project, with the skill definition and the documentation on the tests performed, is available on GitHub:

GitHub logo morriconeluca / skills

A collection of behavior-steering skills for AI agents, optimized to reduce sycophancy and enforce logical consistency.

AI Agent Skills Collection

Skills help modify the behavior, reasoning, and communication patterns of AI agents to make them more effective, direct, and professional collaborators.

Currently, this repository features a core skill focused on communication quality.

Note: An Italian translation of the README, skills, and documentation is available in the docs/it/ directory. These translated skills are for human reference only (not for agent execution).

Featured Skills

1. Non-Sycophantic Communication (non-sycophantic)

AI assistants naturally tend to agree with the user's choices, even when those choices are suboptimal, inefficient, or technically flawed (a phenomenon known as AI sycophancy).

The Non-Sycophantic skill forces the agent to:

  • Maintain a dry, peer-to-peer (equal collaborator) tone.
  • Critically evaluate user requests before answering.
  • Politely but firmly reject mediocre ideas and propose better alternatives.
  • Avoid unnecessary fluff, pleasantries, and conversational filler.

To see how this skill works in practice, view the detailed documentation 👉 Non-Sycophantic Skill…

View on GitHub

Let's Discuss

  • Have you also noticed this tendency of your coding assistants to go along with every choice you make?
  • How would you structure this "pushback loop" to prevent it from diluting in longer chats?

Let me know your thoughts in the comments below!

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