Evaluating AI Coding Agents: A Practical Framework for Code Generation and Long-Horizon Tasks

Background: Agent-Based Coding Has Reached the “Usable” Stage

Over the past year, large language models have made significant progress in code generation. From the early days of “single-line code completion” to today’s ability to scaffold multi-file projects, manage dependencies, and debug and run code, agent-based coding assistants are becoming part of developers’ daily workflows. Major cloud providers and AI platforms have launched intelligent coding services that support long context, file understanding, and tool calling—serving not only professional developers but also individual users with some technical background.

Based on in-depth hands-on experience with a class of agentic coding tools, this article organizes a reusable evaluation framework across dimensions such as code generation quality, long-horizon task capability, multi-session management, UI design skills, and resource quota consumption, and shares typical case studies. Rather than recommending any specific commercial product, it takes a general technical perspective to analyze the current capability boundaries of agent-based coding and the room for improvement.

Designing the Evaluation Dimensions

Objectively assessing a coding assistant requires more than asking “can it write code?” A multi-dimensional evaluation should cover:

Dimension	Key Question	Evaluation Method
Code correctness	Can the generated code run directly?	Actual execution, unit tests
Logical completeness	Are edge cases and exception handling missed?	Code review
Long-horizon task capability	Can it sustain multi-turn progress on complex tasks?	Long task decomposition and tracking
Session isolation	Do concurrent sessions interfere with each other?	Concurrent session testing
Interaction experience	Interface, message queue, interruption mechanism	Hands-on usage
Resource consumption	How quota burn rate relates to project scale	Usage statistics
Visual/UI capability	Are generated interfaces attractive and usable?	Manual evaluation

Each dimension is discussed below.

Code Correctness and Logical Completeness

When using an agent to complete small projects, the generated code is usually “on point” and free of obvious errors. For example, a data structure conversion script, a simple CLI tool, or a static web page will generally pass on the first attempt. However, problems tend to cluster in the following scenarios:

1. Multi-file projects with complex dependencies: Agents frequently make mistakes in module import paths and configuration file locations.
2. Third-party library version differences: Generated code may be based on outdated APIs.
3. Deep algorithmic logic: For example, graph relationship reasoning or causal chains of historical events—these are prone to “hallucination,” producing output that looks plausible but is factually wrong.

Practical advice: Treat the agent as an “advanced code completion plus first-draft generator,” not a “fully autonomous engineer.” Critical logic still requires human review, and factual content (such as character relationships or historical events) must be verified manually.

The Current State of Long-Horizon Task Capability

Agents still show clear shortcomings on long-horizon tasks—those requiring dozens or even hundreds of interactions, spanning multiple sub-goals, and depending on intermediate states. Hands-on testing found:

• Agents can handle fairly complex sub-tasks within a single turn;
• But when a task requires “do A first, wait for the result, then do B, and adjust C based on B,” the agent often cannot maintain state autonomously;
• Progress typically resumes only after a human provides mid-course feedback and correction.

This means fully hands-off automated coding is not yet mature. A more realistic model is human-AI collaboration: humans handle goal setting, key decisions, and result acceptance, while agents handle code generation, repetitive work, and documentation.

Multi-Session Management and Interaction Experience

During concurrent multi-session use, cross-talk between sessions was observed—output from one session leaked into another. This is an engineering-level session isolation defect that significantly hurts the user experience, especially when working on multiple projects at once.

On the interaction side, the message queue mechanism is a plus: it supports appending feedback after the previous round finishes, as well as interrupting immediately. An even better capability would be reading messages intelligently mid-run: when the user sends new instructions, the agent should judge whether to interrupt the current task and prioritize the new message, rather than mechanically queuing it.

UI and Visual Design Capability

On visually oriented tasks such as game UI and character design, current agents generally perform poorly. Generated interfaces often suffer from chaotic layouts and clashing color schemes, and character artwork tends to look bad. This stems partly from the model’s limited grasp of aesthetics and partly from the lack of design-spec constraints.

Improvement ideas: Explicitly specify a design system in the prompt (e.g., color palette, typography rules, component library), or have the agent first produce a design description and then generate the code—this can partially mitigate the problem.

Resource Quotas and Cost Considerations

As a reference point, completing five small projects under one subscription plan consumed only about 1.84% of the quota, indicating that current quotas are quite durable for small- to medium-scale development work. This delivers strong value for individual developers and small teams.

Note, however, that:

• Complex projects, long-horizon tasks, and repeated retries can significantly increase consumption;
• Billing models vary considerably across platforms, so evaluation should be based on actual usage patterns.

Case Studies

Case 1: Character Relationship Map for a Classical Novel

Goal: Generate a complete character relationship map for a novel with a large cast.

Challenges: Complex character relationships, indirect connections, aliases, and hallucination issues.

Process:

1. The first-round map had many gaps;
2. Missing characters were identified manually and additions requested;
3. Clearly incorrect relationships were removed;
4. After multiple iterations, a fairly complete map was obtained.

Conclusion: An agent can help organize relationships, but fact-checking must be done by humans.

Case 2: History-Themed Website

Goal: Build a website introducing a historical period.

Result: The content was fairly generic, several key figures were missing, and depth was lacking.

Conclusion: For tasks requiring deep domain knowledge, agents perform only moderately. They are better suited to framework scaffolding and styling implementation, while content needs human supplementation.

Case 3: Cross-Language Inference CLI Tool

Goal: Build a command-line tool for model inference involving both C++ and Python.

Workflow:

1. Compile the underlying C++ library;
2. Design the Python bindings;
3. Develop the Python package and wrap the CLI.

Conclusion: Agents can significantly improve efficiency in multi-language projects, but compile errors and binding details still require manual debugging.

Summary and Recommendations

Current agentic coding tools have reached the “usable but not fully autonomous” stage. Recommendations for developers:

1. Clarify the division of labor: Agents produce first drafts and handle repetitive work; humans make decisions and accept deliverables;
2. Move in small steps: Break large tasks into verifiable smaller tasks and advance them turn by turn;
3. Prioritize verification: Factual content must be checked manually;
4. Watch the quota: Plan task scale sensibly to avoid waste;
5. Be feedback-driven: Report issues promptly to help the models iterate.

Going forward, as capabilities such as long-term memory, session isolation, and visual understanding improve, agents are expected to take on more complex engineering tasks. Until then, human-AI collaboration remains the most pragmatic model.