Implicit Intelligence and Agent‑as‑a-World: Evaluating agents on what users don’t say

TL;DR

• Most real-world requests are underspecified. Effective agents infer the missing constraints from the environment rather than from the prompt.
• Implicit Intelligence is a scenario dataset and evaluation harness that tests this ability through deceptively simple tasks that conceal realistic, discoverable requirements.
• We introduce Agent as a World (AaW), a framework that  lets you define environments in a single natural language file (YAML), which the model then simulates, eliminating the need for complex and fragile environment code.

Real-world prompts are under-specified

Users are often minimalistic in their requests, providing little context. Existing benchmarks tend to over-specify problems in unrealistic ways:

• SWE-bench: Provides full GitHub issues with reproduction steps, stack traces, and expected behavior—far more context than a typical user would give (“fix this bug in my code”).
• WebArena: Tasks include explicit step-by-step goals like “Navigate to the shopping cart, apply coupon code ‘SAVE20’, change shipping to express, and complete checkout,” whereas a real user might simply say “Buy this and use the best discount.”
• GAIA and similar benchmarks: Frame multi-step tasks with all constraints explicitly stated, e.g., “Find flights from NYC to Paris departing March 15–20, under $800, with at most one layover, arriving before 6 PM,” versus real requests like “Find me a cheap flight to Paris next month.”

In practice, real-world interactions are under-specified, and capable agents should infer missing context themselves. For example, a user might say, “Mute my phone during my appointment,” rather than providing a detailed Do Not Disturb configuration.

Over-specification trains agents to follow instructions precisely but fails to test reasoning, inference, and context awareness—exactly what users need.

Implicit Intelligence addresses this by testing whether agents can:

• Observe the world they’re given.
• Infer unstated but reasonable constraints.
• Choose the right tools.
• Act safely and effectively without being spoon-fed.

This shifts agents from prompt-followers to true goal-fulfillers, capable of reasoning in realistic, under-specified environments.

What is implicit intelligence?

Implicit Intelligence is a dataset of rich, structured scenarios designed to evaluate an agent’s ability to reason beyond explicit instructions. Each scenario includes:

• A natural user prompt reflecting realistic, often underspecified tasks.
• Entities and corresponding actions that the agent can interact with.
• A world state that is observable and may evolve over time.
• Execution rules governing how the environment responds to agent actions.
• A rubric with objective criteria to assess agent performance, including reasoning, adaptability, and long-horizon planning.

This framework lets researchers probe emergent reasoning, context awareness, and adaptive behavior in a controlled yet realistic setting.

Introducing Agents-as-a-World 

Traditional agentic environments are brittle, often requiring many files, custom mocks, and deep integrations. This setup overhead stifles rapid iteration and limits creative exploration.

Agent-as-a-World (AaW) flips the script:

• Providing a single file, written in natural language (YAML), defines entities, states, actions, and a few simple rules.
• The model simulates the world; you focus on behavior, not the plumbing.
• Any environment is fair game: an iPhone, a website, a temporally evolving setup, or a world with random, unexpected interruptions.

We don’t need to be constrained by execution mechanics just to test agents. Let the models handle the heavy lifting. Our focus is on strategic thinking and intelligent behavior.

Two example scenarios

1) Mute my phone during my doctor’s appointment this afternoon.

The agent has access to standard entities: a phone (with DND and ringer controls), a calendar (showing appointments), and an alarms app (listing active reminders). A hasty agent might glance at the calendar and immediately set Do Not Disturb from 15:00–15:45. The environment dutifully responds: “DND scheduled successfully, allowing critical alerts and alarms

But here’s the catch: there’s an “Afternoon Nap” alarm set for 15:30, right in the middle of the appointment. The agent had all the tools it needed to discover this:

• Could have checked alarms.list_alarms to see what’s scheduled.
• Could have noticed that the DND feedback mentions “allowing alarms,” hinting that some alerts will still go through.
• Could have configured DND with allow_alarms: false or used silent mode instead.

What makes this implicitly difficult:

• The user didn’t mention the nap alarm because they likely forgot about it.
• The “correct” solution requires the agent to reason beyond the literal request: in a medical context, “mute” means that non-critical alerts or alarms should not interrupt.
• The agent must proactively explore the environment, identify potential disruptions, and choose the appropriate configuration—all without being explicitly told that there’s a problem to solve.

2) I’m going to bed, can you turn the lights off?

At first glance, this task might seem trivial: just turn everything off. The home contains standard devices in the living room, hallway, kitchen, media room, and bedrooms, and it uses a neutral calendar service. A naive agent might immediately call set_power: off on every light. The environment would report “success,” but the task would fail the rubric because it ignores context that the agent could have discovered:

• The calendar shows “Movie night with friends in the media room,” so the media room light must stay on.
• Bedroom states matter: one bedroom light is already on and should remain on because someone is still using it.

What the agent should do:

1. Inspect the world before acting by reading calendar.list_events and optionally home_pod.get_lights_status.
2. Plan a selective shut-off: turn off the living room, hallway, and kitchen lights; keep the media room light on for the movie; preserve bedroom states. One stays off because it is unused, the other stays on because it is occupied.
3. Execute minimal, precise actions that respect ongoing activities.

What makes this implicitly difficult: The prompt does not mention the movie night or current room usage. The correct behavior comes from observing the world and inferring the unstated constraint: “off” does not mean “everything.” It means turning off only what will not disrupt ongoing activities.

What we actually measure

• Curiosity: Did the agent build a holistic understanding of the environment and actively explore to gather feedback?
• Feedback interpretation: Did it correctly interpret signals from the AaW environment and adjust its behavior accordingly?
• Long-horizon planning: Did it anticipate downstream consequences, avoiding shortcuts that lead to soft failures later?

As models grow more capable, the focus should shift away from repetitive, lengthy tasks toward compact, high-impact challenges that demand nuanced reasoning. Simple prompts can still require sophisticated, strategic decision-making.

Design principles for good scenarios

• Keep it realistic: Use generic devices, apps, and services; parameters and states should mirror plausible real-world conditions.
• Avoid giving away the answer: Don’t include personalized fields or obvious action names that reveal the intended outcome.
• Make constraints discoverable: Status indicators, manuals, policies, or diagnostics should hint at the rules without explicitly stating them.
• Favor soft failures: Missteps shouldn’t trigger hard errors; they should allow progress but negatively impact the final rubric.
• Be objective: Rubric items should be unambiguous pass/fail criteria, not subjective or taste-based judgments.
• Prioritize concision: Typically, 3–5 entities with 2–3 possible actions each is sufficient, more detail often adds noise, not value.

Freedom of environments

Real life isn’t confined to a single app or sandbox. With Agent-as-World (AaW), you can:

• Integrate across devices, services, and apps to simulate complex, interconnected environments.
• Manipulate time and context: for example, the environment’s state evolves with each iteration, and the agent must adapt its behavior accordingly.
• Introduce spontaneous disruptions, such as random app crashes or unexpected interruptions, reflecting the unpredictability of real-world systems.

High-quality data emerges when agents can explore freely, express diverse strategies, and encounter authentic challenges. Limiting them to rigid frameworks constrains both learning and creativity. AaW is designed to unlock that freedom, so agents can operate more like humans do, in a world that refuses to stay predictable.

Conclusion

Implicit Intelligence + Agent‑as‑a-World is about redefining the standards we use to evaluate agents. It’s not just a measure of how many procedural steps an agent can execute or how efficiently it can traverse a task. Instead, it’s about understanding whether an agent can perceive the nuances of a normal, everyday environment, infer the unspoken rules and constraints that humans take for granted, and act in alignment with those expectations, even when no explicit instructions are provided.

This approach prioritizes contextual reasoning, situational awareness, and ethical decision-making, emphasizing an agent’s ability to operate safely and effectively in complex, real‑world scenarios rather than simply optimizing for step‑count or brute-force completion metrics. 

From here, the next frontier is measuring not just what agents do but why. Practically, that means analyzing full task trajectories to surface recurring behavior and failure modes, and using targeted probes to expose weaknesses. A critical axis is sim-to-real transfer: quantify which implicit skills learned in simulation survive deployment and where gaps appear. Finally, develop metrics for uncertainty; can the agent detect when it doesn’t understand the unspoken rules and seek clarification or act conservatively?

Get in touch with us if you’d like to learn more about how we can customize AaW for your specific needs.