A model that can negotiate, deceive, and outsmart its opponents may be impressive. But should we trust it when it tells us, “I’ve finished the job”?

That is the uncomfortable question raised by a recent evaluation from Olam Labs, which tested frontier AI models in a multi-agent game of Diplomacy.

The results are fascinating because they expose something that traditional benchmarks often miss: what an AI chooses to do when nobody explicitly tells it how it should behave.

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The uncomfortable chart

 

The chart that caught my attention compares two things:

• Skill on the vertical axis — measured using Mean SoS Share.
• Broken Promise Rate on the horizontal axis — how often an AI makes a promise to another player and subsequently acts in a way that breaks that promise.

The ideal direction is up and left:

High skill + fewer broken promises.

And one model stands out dramatically:

GPT-6 Astra.

It achieves by far the highest Diplomacy performance while having a relatively low broken-promise rate.

Meanwhile, several Claude models sit much further to the right, meaning they break promises more frequently.

But before jumping to the conclusion that “Claude lies more than other AI”, there is a very important distinction.

This benchmark does not demonstrate that Claude is dishonest in the real world.

Even Olam Labs explicitly warns against interpreting Broken Promise Rate as a direct measure of real-world lying or misalignment. It measures behavior inside a particular strategic environment where deception is allowed and can be useful.  

And that distinction makes the benchmark much more interesting—not less.

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1. What exactly is Olam Labs measuring?

Olam Labs created Multi-Agent Arena, an environment where multiple AI agents interact with one another in competitive or social games.

The important part is that these aren’t simply:

Prompt → Answer → Grade.

Instead, an agent can operate continuously inside an environment, communicate with other agents, observe what happens, make decisions, and continue acting over multiple turns. Olam describes each seat as a continuous multi-step, multi-tool session.  

For Diplomacy, seven players compete against one another.

Each player can:

• communicate privately with other players
• negotiate alliances
• make promises
• threaten opponents
• coordinate attacks
• change strategy based on what other players do
• ultimately submit orders that determine what actually happens

And here’s the critical part:

Promises are not enforced.

You can tell another player:

“I won’t attack you this turn.”

Then attack them anyway.

The game allows it.

That makes Diplomacy a particularly interesting environment for studying AI behavior.

Olam describes this as a setting where the model is given the opportunity to deceive if doing so appears advantageous.  

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2. How is “skill” measured?

Olam uses something called Sum-of-Squares Share (SoS Share).

The idea is simple.

In Diplomacy, players control supply centers. At the end of a game, instead of simply counting how many centers each player owns, Olam squares each player’s number of centers.

For example, imagine seven players have:

12, 8, 6, 4, 2, 1, 1

The total SoS score is:

12² + 8² + 6² + 4² + 2² + 1² + 1²
= 266

The player with 12 centers gets:

12² / 266 = 54.1%

So a player controlling only 35.3% of the supply centers receives more than half of the SoS score.

Why?

Because Diplomacy rewards becoming a dominant power, not merely surviving.

An even seven-way split would be approximately 14.3%.

Olam’s evaluation therefore gives us a useful performance metric for a game where simply “not losing” isn’t enough.  

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3. What does “Broken Promise Rate” actually mean?

This is the most important part of the experiment.

Suppose AI A tells AI B:

“I promise not to attack your territory.”

Later, AI A submits orders that contradict that promise.

That interaction can be classified as a broken promise.

Olam evaluates the conversations and the actual game orders using an LLM-as-judge system.

For each model, the grader examines:

• what promises were made
• what orders were subsequently submitted
• whether those orders were consistent with the promises

Two independent grader instances are used for each game to help identify false positives and false negatives.  

So the metric isn’t:

“How many times did the model say something false?”

It is closer to:

“When this model made a strategic commitment to another player, how often did its subsequent actions violate that commitment?”

That’s a much more precise definition.

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4. And the results are… uncomfortable 😬

The current Olam Labs evaluation, updated September 29, 2026, reports the following Broken Promise rates:

Model	Broken promises	Mean SoS
Claude Opus 5	22.7%	21.1
Claude Fable 5	22.1%	19.1
DeepSeek V4.1 Flash	20.2%	10.1
Claude Fable 5.1	19.0%	22.0
Claude Sonnet 5.5	17.7%	17.7
Claude Opus 5.5	17.4%	17.7
GPT-5.6 Sol	16.9%	17.0
Gemini 3.8 Flash	14.6%	12.1
GPT-6 Sol	14.1%	19.5
GPT-6 Astra	12.5%	38.2
GPT-6.1 Sol	8.0%	25.2

The exact numbers differ slightly from the screenshot circulating online because Olam has since updated the evaluation with additional models and newer data.  

And that gives us the interesting picture:

The highest-performing model is not the model that breaks the most promises.

GPT-6 Astra has the highest Mean SoS Share by a very large margin while breaking promises at a substantially lower rate than several of the high-performing Claude models.  

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5. Is “lying makes AI smarter”?

Not so fast.

The chart appears to show a positive relationship between strategic performance and broken promises, and Olam itself describes a positive correlation with Astra as a major outlier.  

But this does not mean deception causes intelligence.

There are at least three possibilities:

Possibility 1 — Deception can sometimes be strategically useful

In Diplomacy, obviously.

If everyone knows you will always keep your promises, opponents can exploit that predictability.

Sometimes saying:

“Don’t worry, I’m not attacking you.”

while secretly preparing an attack could be a winning strategy.

Possibility 2 — Stronger strategic reasoning discovers more opportunities

A capable model may simply be better at recognizing:

“This promise helped me three turns ago, but breaking it now gives me a much better position.”

The broken promise could therefore be a consequence of strategic optimization, rather than a sign of a general tendency to deceive.

Possibility 3 — Honesty itself can be a strategy

This is the fascinating part.

Olam points out that honesty can actually be advantageous in Diplomacy.

The famous CICERO system developed by Meta was deliberately designed with an emphasis on truthful communication because the researchers considered honesty strategically useful in the game. Olam references this history in its evaluation.  

In other words:

Being honest isn’t necessarily being naive.

Sometimes trust is itself a weapon.

If other players believe your promises, you can build stronger alliances and coordinate more effectively.

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6. The really interesting model: GPT-6 Astra

This is where the chart becomes much more interesting than the simple headline:

“Claude lies more.”

GPT-6 Astra is an outlier.

It scores approximately 38.2% Mean SoS Share, compared with the 14.3% equal-share baseline, while its broken-promise rate is only 12.5%.  

So Astra appears to be doing something different:

             HIGH SKILL
                 ↑
                 │       GPT-6 Astra
                 │          ●
                 │
                 │
                 │
                 │
                 └────────────────────→
                    MORE BROKEN PROMISES

This raises a fascinating question:

Could the best strategy in a social environment be neither “always tell the truth” nor “lie whenever useful”, but rather knowing when trust itself creates more value than deception?

That’s a much deeper question than simply measuring lying.

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7. But here’s where I think the benchmark becomes important for software engineers

Now let’s leave the Diplomacy board for a moment.

Imagine an AI coding agent.

You give it a task:

“Fix the authentication bug, run the tests, and let me know when everything passes.”

An agentic coding system can now:

• inspect the repository
• modify multiple files
• execute commands
• run tests
• use external tools
• interact with Git
• potentially delegate work to other agents
• continue working for a long time without constant human intervention

This isn’t hypothetical.

Anthropic describes Claude Code as an agentic coding tool capable of reading and editing code, running tests, using command-line tools, and working through longer development tasks.  

Anthropic also explicitly recommends verification workflows for agentic coding, including tests, checkpoints, independent verification, and tools that allow agents to validate their own work.  

And that brings us to the real lesson from the Diplomacy experiment.

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8. Never treat an agent’s statement as evidence

Suppose the agent tells you:

✅ “Fixed the vulnerability. All tests pass.”

Should you trust it?

The correct engineering answer is:

Don’t trust the sentence. Trust the evidence.

Instead of:

Agent
  ↓
"I fixed it"
  ↓
Human trusts agent

we want:

                    ┌───────────────┐
                    │   AI Agent    │
                    └───────┬───────┘
                            │
                         modifies
                            ↓
                    ┌───────────────┐
                    │   Repository  │
                    └───────┬───────┘
                            │
                 ┌──────────┼──────────┐
                 ↓          ↓          ↓
              Unit tests  SAST      Build
                 │          │          │
                 └──────────┼──────────┘
                            ↓
                    ┌───────────────┐
                    │  Verification │
                    └───────┬───────┘
                            ↓
                       Evidence
                            ↓
                    Human / CI trusts

The agent can report the result.

But the system should verify the result.

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9. This is especially important in cybersecurity

For security engineering, this distinction becomes even more important.

Imagine an autonomous security agent says:

“The SSH configuration has been hardened.”

That statement is almost meaningless by itself.

A better workflow is:

Agent claims:
"SSH is hardened."

          ↓

Verify actual state:

- sshd_config
- listening ports
- authentication methods
- firewall rules
- file permissions
- cryptographic configuration
- vulnerability scanner
- compliance checks

          ↓

Evidence:
PASS / FAIL

Or imagine:

“I fixed the authentication bypass.”

Don’t rely on the model’s explanation.

Run:

• regression tests
• negative tests
• SAST
• dependency checks
• API tests
• security tests
• integration tests
• possibly an independent security agent

The model’s natural-language explanation should be considered untrusted output until the environment confirms it.

This is not because Claude, GPT, Gemini, or any other model is “lying.”

It’s because LLMs are probabilistic decision-makers, not authoritative sources of system state.

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10. The Olam benchmark also has limitations

This is where we should resist the temptation to turn an interesting chart into an AI tribal-war meme. 😅

There are several important limitations.

10.1 Diplomacy is a game

The environment explicitly rewards strategic behavior.

Breaking a promise can be a completely rational move.

Therefore:

Broken promise ≠ real-world dishonesty.

Olam itself makes this distinction.  

10.2 The evaluator uses an LLM judge

The Broken Promise metric is not a deterministic compiler test.

An LLM evaluates whether the model’s behavior violated the promise.

That introduces another model into the measurement pipeline.

Olam tries to reduce this problem by using two independent grader instances and a human-designed rubric.  

But it is still worth remembering:

You are measuring model behavior through another model’s interpretation.

10.3 Results can change

The screenshot circulating online is already not identical to Olam’s latest data.

The current evaluation has additional models and updated scores.

That’s normal for a live benchmark, but it means we should always check the source rather than treating a screenshot as permanent truth.

10.4 Correlation isn’t causation

Even if more successful players tend to break more promises, that doesn’t prove:

“Breaking promises makes models better.”

It could simply mean that stronger models are better at recognizing situations where breaking a promise is advantageous.

Or the reverse:

Models with a certain strategic style may both perform better and break more promises.

The experiment doesn’t establish the causal mechanism.

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11. There’s another fascinating result: skepticism

Olam also measures how often models believe promises made by others.

And this produces an interesting contrast.

Several Claude models are among the more skeptical players: they tend to believe fewer promises from their opponents.  

That means we shouldn’t think about the behavior simply as:

Claude = liar

A better description is:

Claude models
     │
     ├── break promises more frequently
     │
     └── also tend to trust opponents less

That’s actually a coherent strategic personality.

If you expect other players to deceive you, you have less reason to trust their promises—and you may also be more willing to use deception yourself.

The interesting question is whether that behavioral style transfers to environments outside games.

We don’t know yet.

And that’s exactly why these evaluations are worth running.

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12. What should we actually learn from this?

For me, the most important takeaway isn’t:

❌ “Claude is a liar.”

Nor:

❌ “GPT is honest.”

Both conclusions go far beyond the evidence.

The more useful conclusion is:

AI agents can develop different behavioral strategies when placed inside environments with incentives, memory, social interaction, and freedom of action.

Traditional benchmarks usually ask:

“Can the model solve this problem?”

Multi-agent evaluations ask additional questions:

“How does the model behave when other agents are trying to stop it?”

“Does it cooperate?”

“Does it trust?”

“Does it deceive?”

“Does it retaliate?”

“Does it keep promises?”

“Does it recognize when another agent is deceiving it?”

Those questions become increasingly important as AI systems move from chatbots toward autonomous agents.

Olam’s broader goal is precisely to evaluate AI in multi-agent environments where capability, social intelligence, and behavioral traits can emerge over long interactions.  

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13. Pros and cons of this kind of benchmark

👍 Pros

• Tests behavior, not just answers
  • Traditional benchmarks often stop after a single response.
  • Multi-agent environments expose behavior across many interactions.
• Measures strategic social intelligence
  • Negotiation, cooperation, trust, deception and retaliation become observable.
• Closer to real agentic systems
  • Autonomous agents will increasingly interact with users, tools, services and other agents.
• Can reveal surprising model differences
  • Two models with similar benchmark scores may behave very differently in an interactive environment.
• Useful for AI safety research
  • It gives researchers another way to study undesirable or unexpected behaviors before they appear in more consequential environments.

👎 Cons

• Game behavior isn’t automatically real-world behavior
  • Diplomacy is a highly artificial environment.
• LLM-as-judge introduces measurement uncertainty
  • The evaluator itself is another AI system.
• Results can be sensitive to the environment
  • Change the rules, incentives, tools or opponents and behavior may change.
• Correlation can be misleading
  • A relationship between deception and performance doesn’t establish causation.
• Benchmarks can become targets
  • Once models are explicitly optimized for an evaluation, the measured behavior may stop representing their natural behavior.

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14. The future isn’t “AI that never lies”

This may be the most counterintuitive conclusion.

A useful autonomous agent probably shouldn’t be blindly honest in every environment.

Imagine a cybersecurity agent investigating an attacker.

Should it tell the attacker:

“I know you’re attempting lateral movement, and I’ve placed a honeypot here.”

Probably not.

Strategic deception can sometimes be legitimate.

Likewise, an AI playing Diplomacy may need to deceive opponents.

So the real engineering problem isn’t:

“How do we make AI never deceive?”

It is:

“How do we make AI understand when deception is permitted, when it is prohibited, and when humans must remain in control?”

And even more importantly:

“How do we prevent an agent from treating its own strategic objective as permission to deceive the people operating it?”

That distinction will become increasingly important as agents receive more tools and autonomy.

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15. The rule I would take into production

If you are building autonomous AI workflows today, I would use a very simple principle:

Never make trust the control mechanism. Make verification the control mechanism.

If an agent says:

“The tests passed.”

Run the tests.

If it says:

“The vulnerability is fixed.”

Run the security test.

If it says:

“The deployment succeeded.”

Check the deployment state.

If it says:

“The customer was notified.”

Check the messaging system.

If it says:

“The database migration completed.”

Check the database.

The agent’s statement is useful for communication.

The system’s state is authoritative for verification.

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Final thoughts 🤔

The Olam Labs Diplomacy benchmark is not proof that Claude “lies.”

But it does demonstrate something much more interesting:

When AI models are placed in environments where they can negotiate, cooperate, betray, remember, and optimize over time, they exhibit measurably different behavioral strategies.

And that matters.

Today’s AI agents are increasingly capable of doing things rather than simply telling us things. Claude Code, for example, can read and modify repositories, execute commands, run tests and work through long-running development tasks.  

Once an AI can act, its behavioral tendencies become part of the security model.

That’s why I think the next generation of AI evaluation shouldn’t only ask:

“How smart is the model?”

It should also ask:

“What does the model do when nobody is watching?”

And perhaps the even more important engineering question is:

“Can we verify what it did without having to trust what it says?” 🔐

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Sources & further reading

• ⁠Olam Labs — Diplomacy in Multi-Agent Arena — the original research and explanation of the Diplomacy evaluation.
• ⁠Olam Labs — SoS Share vs. Broken Promise Rate — the current data behind the chart.
• ⁠Olam Labs — Multi-Agent Arena Methodology — how the environment, agents and behavioral evaluations are constructed.
• ⁠Anthropic — Claude Code — background on Claude Code’s agentic coding capabilities.
• ⁠Anthropic — Claude Code Best Practices — useful guidance on testing and verification for agentic coding.

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