From Answering Questions to Executing Tasks: An Engineering Framework for Assessing AGI

When a new generation of general-purpose models is released, discussions often focus on whether its scores on comprehensive benchmarks have improved substantially. Many people use a single overall score to judge whether a model represents a generational leap. If the score barely changes, they may conclude that the model is simply larger or more polished—or question whether it deserves to be called a next-generation model.

That way of judging progress is becoming less adequate.

For earlier models, answering a question, generating a piece of code, or summarizing a document usually marked the end of the task. Static prompts could provide a useful measure of a model’s core capabilities. But as models take on complex research, software development, data analysis, IT operations, and multi-step business processes, the important question is no longer just whether an answer is correct. It is whether a system can understand a goal, plan its approach, use tools, handle failures, and see the work through to completion.

This may be the most important shift in the next generation of general-purpose models: the focus is moving from answering questions to completing tasks over time.

1. Why Overall Benchmark Scores May Not Rise Significantly

Comprehensive evaluations typically combine tests of mathematics, general knowledge, language understanding, logical reasoning, and coding. They are effective for comparing baseline capabilities, but they have clear limitations.

First, many benchmark tasks are single-turn, static, and self-contained. The model reads an input and produces an answer. It does not need to maintain state over an extended workflow, deal with failures in external systems, or contend with permissions, networks, data formats, and changing environments.

Second, some difficult benchmarks are approaching the limits of current models. When several models can already answer most of the easier questions, newly acquired capabilities may be hard to see in the aggregate score. As in an exam where many candidates score close to the maximum, average results can obscure meaningful differences.

Third, overall scores tend to emphasize local outcomes, while complex work depends on the quality of the entire process. A model might write a correct function but fail to install dependencies, run tests, diagnose errors, and deliver a usable result. It might propose a research approach but fail to gather sources, verify them, or recover from mistakes along the way.

Stable benchmark scores therefore do not necessarily mean that a model has stopped improving. Its basic question-answering ability may have plateaued while progress is happening in task organization and execution.

2. The Generational Shift: From Content Generation to an Execution Loop

A capable intelligent system generally needs to complete a loop:

1. Understand the goal.
2. Break the task into steps.
3. Create an execution plan.
4. Call external tools.
5. Observe the tools’ results.
6. Adjust its approach based on those results.
7. Verify the final output.
8. Ask the user for more information when necessary.

Traditional conversational models often stop after the first few steps—or end the task when a tool call fails. More capable systems treat a task as an ongoing process rather than a one-time text-generation request.

Consider a user asking a system to analyze a set of business data and recommend improvements. A useful system cannot simply produce a plausible-looking report. It must confirm the data format, identify missing fields, run the analysis, detect outliers, explain the basis for its conclusions, and describe the risks of its recommendations. If data loading fails, it should determine whether the cause is a permissions issue, an incorrect path, or an unsupported format rather than simply replying that it cannot proceed.

This capability is better measured by a task completion rate than by the accuracy of a single answer.

3. Why Long-Running Tasks Are Difficult

The challenge of long-running tasks is not just the size of the context. The system must also manage state reliably.

3.1 Goals Can Drift

The more information generated during a task, the easier it is for a model to lose sight of the original objective or get sidetracked by a local issue. A system therefore needs to retain the goal, constraints, completed steps, and outstanding items, and periodically check that its work is still on track.

3.2 Errors Can Compound

An error in a short response may affect only a sentence. In a long task, an early mistake can undermine everything that follows. For example, misunderstanding a data field can invalidate the resulting statistics, visualizations, and conclusions.

Reliable systems need checkpoints to validate intermediate outputs at critical stages, rather than waiting until the end to discover that the process has gone off course.

3.3 External Tools Are Unreliable

Files, APIs, databases, and execution environments in real-world settings can all fail. A tool call may time out, return no data, lose access permissions, or produce an unexpected format.

A model must do more than know how to call a tool. It must interpret the tool’s response and distinguish between errors that can be retried, errors that require different parameters, and errors that need human intervention.

3.4 Results Must Be Verifiable

Long-running tasks should not rely solely on a model’s own judgment. Results can be checked through tests, rules, data reconciliation, format validation, and human approval. Code tasks, for example, should run tests; data tasks should verify record counts; and automated workflows should log the inputs and outputs of each step.

4. What Does It Mean to Be “Close to AGI”?

AGI is not a label that can be confirmed by a single score. A more useful approach is to assess whether a system demonstrates a set of stable, transferable, and sustained general capabilities.

General Problem-Solving Ability

Can the system break an unfamiliar problem into actionable steps, rather than relying on a similar template from its training data? Can it transfer its methods when the problem’s form changes?

Adaptability Across Domains

A genuinely general-purpose system should not be limited to writing or programming. It should be able to move among research, analysis, planning, coding, and business workflows while accounting for the constraints of each domain.

Autonomous Error Correction

When its first attempt fails, can the system identify why, propose a different approach, and try again? This is closer to the demands of real work than producing a correct answer on the first attempt.

Tool Coordination

Can the model select appropriate tools, pass the right parameters, interpret the results, and connect multiple tools into a coherent workflow? The number of tool calls is not what matters. The calls must be necessary, accurate, and controlled.

Stability Over Long Tasks

Can the system maintain a consistent goal, clear state, and traceable results during a task that lasts hours or longer? This is often an important distinction between a system that can answer and one that can work.

AGI should therefore not be understood simply as achieving high scores on every exam. It is more useful to think of it as the ability to solve a broad range of problems reliably in open-ended environments.

5. Metrics Enterprises Should Consider

Organizations evaluating intelligent systems should build assessments around their own work rather than relying solely on peak scores in promotional materials.

Start with task completion rate. Measure whether the system can complete a task independently within a defined time, not just whether its intermediate answers are correct.

Next, measure human intervention rate. If a workflow needs human confirmation every few steps, its automation value may be limited.

Also track average recovery cost. How much time does it take a person to correct a failure? If a minor error requires the entire workflow to be run again, the actual cost may still be high.

Consider consistency of results as well. When the same task is run multiple times, are the outputs stable, or do they vary significantly?

Finally, assess auditability. Organizations need to know which data the system used, what steps it performed, which tools it called, and what evidence supports its conclusions. Automation without a process record is difficult to use in high-risk production environments.

6. A Practical System Architecture

A reliable intelligent task system can typically be divided into five layers:

1. Interaction layer: Receives the user’s goal and gathers additional information while enforcing permission boundaries.
2. Planning layer: Breaks complex goals into steps and defines each step’s inputs, outputs, and completion criteria.
3. Execution layer: Uses general capabilities such as data processing, code execution, search, and file operations.
4. Verification layer: Checks that results meet format, logic, data, and safety requirements.
5. Governance layer: Handles access controls, logging, sensitive data protection, emergency stops, and human approval.

A simplified workflow looks like this:

Receive the goal
  ↓
Identify constraints and risks
  ↓
Generate a task plan
  ↓
Execute one step
  ↓
Check tool results
  ↓
Validate intermediate outputs
  ↓
Revise or continue
  ↓
Return the final result and process record

This structure helps improve overall reliability through validation, retries, state management, and permission controls—even when the underlying model’s capabilities vary.

7. Greater Capability Must Not Obscure Safety Risks

As models become more capable of execution, their potential risks increase. Systems that can modify code, access data, or perform actions need clear permission boundaries.

1. Apply the principle of least privilege. Give the system only the permissions required for the current task, rather than default access to all files and business data.
2. Require approval for high-risk actions. Deleting data, sending external messages, changing production configurations, and carrying out financial actions should require human approval or a second confirmation.
3. Separate trusted instructions from untrusted content. External documents, web pages, and uploaded files may contain manipulative instructions. The system must not treat untrusted content as a higher-priority command.
4. Keep an end-to-end audit trail. Record every tool call, parameter change, error-handling step, and human intervention to support auditing and accountability.
5. Allow tasks to be stopped at any time. Long-running tasks must support pausing, rollback, and termination. A system should not retry indefinitely when something goes wrong.

Conclusion: Judge Next-Generation Models by Their Ability to Do Real Work

The value of a new generation of general-purpose models may not first appear as a dramatic improvement on comprehensive benchmarks. More revealing signs are whether a system can handle open-ended goals, maintain state over time, use external tools, recover from failures, and deliver results that can be verified.

A system that can only produce polished answers is still closer to an advanced question-answering tool. A system that can plan, execute, check, and correct its work toward a goal is beginning to resemble a general intelligent system.

So, when assessing whether a model is moving toward AGI, do not ask only, “What is its overall score?” Also ask:

• Can it complete complex tasks independently?
• Can it transfer its capabilities to unfamiliar environments?
• Can it identify and correct errors?
• Can it operate reliably within safe boundaries?

The answers are found not in a single demonstration or benchmark leaderboard, but in the quality of completed real-world tasks, the reliability of the process, and the system’s long-term controllability.