Evaluating AI Agent Capabilities: From Single-Task Execution to Multi-Scenario Collaboration

Introduction: Moving Beyond Conversational AI

Over the past year, industry discussions around AI agents have intensified, yet few systems can reliably handle complex tasks in real-world production environments. Many products deliver impressive demos but struggle with task decomposition, unstable tool invocation, or unusable outputs once deployed in actual scenarios.

This evaluation does not target any specific commercial product. Instead, it validates common AI agent architectures across several general capability dimensions. The core question is straightforward: to what extent can a system built on modern agent frameworks perform in information retrieval, content generation, code development, visual design, and multi-task collaboration? Which scenarios are ready for direct deployment, and which still require human fallback?

To answer this, we designed nine distinct task scenarios spanning multiple complexity levels, from simple Q&A to complex system construction. All tasks were described in natural language without additional structured prompts or human intervention, simulating how ordinary users and developers interact with these systems.

Test Scenario Design: Nine Tasks to Probe Capability Boundaries

Task selection followed two principles: relevance to actual workflows, and the ability to reveal core capability differences across agent systems. The nine scenarios fall into four categories:

Information Retrieval and Content Generation

1. Web-wide information retrieval with structured summarization
2. Automatic generation of long-form technical documentation
3. Presentation deck creation for specific audiences

Code Generation and System Construction

1. Static page generation with visual optimization
2. Basic full-stack application architecture setup
3. Scripting for 3D interactive scenes

Design Creativity and Visual Expression

1. Visual style implementation from text descriptions
2. Dynamic backgrounds and shader effect generation

Complex Collaboration and Workflow Orchestration

1. Multi-agent division of labor for comprehensive tasks

This classification enables fine-grained evaluation of agent performance across different dimensions rather than a binary “good” or “bad” verdict.

Information Retrieval and Content Generation: From Finding to Structuring

The first category tests an agent’s ability to gather and organize information. Traditional search tools solve the problem of “finding” information; the value of an agent lies in further filtering, synthesizing, and producing structured output.

In the web-wide retrieval task, we asked the system to collect biographical information about a public figure and organize it by timeline, major achievements, and representative events. The system returned a structured summary within a reasonable time, with solid timeline completeness and accuracy. However, some details needed improvement: precision of specific dates, insufficient authority of certain sources, and immature neutral handling of evaluative statements about the subject.

The long-form technical document task pushed further on logical organization. We requested a practical guide on observability for distributed systems, including concept explanation, architecture design, implementation steps, and common pitfalls. The system performed well on concept explanation and procedural steps, but the architecture design section leaned toward generic “one-size-fits-all” solutions, lacking trade-off analysis tailored to specific business contexts. This suggests that while current agents have made significant progress in knowledge breadth, they still lag in knowledge depth and scenario-specific judgment.

The presentation task produced similar “broad but shallow” results. The system generated well-structured, logically laid out slides with clear hierarchy and accurate key-point extraction. However, deep-diving into any single slide or adjusting expression style for a particular audience required multiple interaction rounds to reach satisfactory output.

Code Generation and System Construction: From Writing to Running

Code generation is currently one of the most mature areas of agent capability and the focus of this evaluation.

For static page generation, we provided a deliberately vague requirement: generate a personal profile page with a high-end aesthetic. The system returned complete page code, including semantic HTML, responsive layout, and basic interactive effects. The page reached a “usable” visual standard but fell short of “polished.” Notably, when we added more specific visual requirements in subsequent rounds—such as color scheme, typography style, and animation rhythm—the system iterated quickly and demonstrated strong instruction-following capability.

The full-stack application task tested engineering proficiency more comprehensively. We asked for a simple application framework with user authentication, data storage, and basic APIs. The generated code showed clear structure, reasonable module separation, and basic security practices such as parameterized queries for database operations. However, several issues emerged: unspecified dependency versions, sensitive configuration values not isolated via environment variables, and insufficiently robust error handling. These findings indicate that agents can generate “runnable code” for simple scenarios, but production-grade delivery still requires human review and hardening.

The 3D interactive scene scripting task was technically the most demanding. We requested a browser-based fluid effect background with specific color and animation parameters. The system successfully generated vertex and fragment shader code, with core noise functions and color blending logic largely correct. However, rendering performance optimization was lacking—there was no fallback strategy for low-end devices, and computational precision settings in the shaders were not optimally configured.

Visual Design and Motion Effects: Bridging “Visible” and “Beautiful”

Visual design tasks serve as an important measure of an agent’s creativity and aesthetic judgment. In this evaluation, we focused on two dimensions: understanding and executing design style, and technical implementation of dynamic effects.

For the visual style task, we provided a brand positioning description covering primary colors, lighting direction, and overall atmosphere. The system’s understanding and execution were largely accurate, converting natural language into specific color values, light source parameters, and material properties. The output showed harmonious color coordination, but details retained a mechanical feel—light transitions were not fully natural, and material rendering lacked depth.

The dynamic background task yielded similar results. The system correctly interpreted a requirement like “slow breathing” motion and translated it into loop duration and easing function parameters. The shader code had clear logical structure and well-named core variables. However, the visual rhythm felt rigid, and easing curve choices lacked subtlety. These “last-mile” refinements typically require a designer or experienced developer to adjust.

Multi-Agent Collaboration: From Solo to Teamwork

Multi-agent collaboration is a key direction in agent technology evolution. The core idea is to have multiple agents with different roles work together to solve tasks beyond the capability of any single agent.

For this evaluation, we designed a comprehensive task: produce a market analysis report including data collection, trend assessment, report writing, and slide deck creation. The system decomposed the task into four sub-processes, each handled by a dedicated agent role, with intermediate results passed through structured data formats.

Overall, the collaborative pipeline did improve efficiency for complex tasks. Data collection outputs informed subsequent analysis, and report and slide styles remained consistent. However, typical issues also surfaced: information loss during intermediate handoffs, concept drift between different roles, and a lack of conflict detection mechanisms. These problems become more pronounced in larger, longer-running workflows.

Summary and Recommendations: Defining Capability Boundaries

Based on our nine-scenario evaluation, we can form a clear judgment about current AI agent capabilities:

Areas with practical value today: information retrieval with structured output, static page generation, basic code writing, and structured documentation. In these scenarios, agents can serve as direct efficiency tools, significantly reducing repetitive work.

Areas requiring deep human involvement: full-stack system development, 3D interaction implementation, refined visual design, and multi-agent workflow orchestration. In these cases, agents handle substantial foundational work, but key decisions, quality control, and exception handling still require professionals.

Recommended development practices: treat agents as “advanced assistants” rather than “full replacements.” For code generation, establish code review and automated testing pipelines. For content generation, retain human review checkpoints. For multi-agent scenarios, design explicit result validation and rollback mechanisms.

AI agent technology is advancing rapidly, from conceptual demonstrations six months ago to partially usable systems today—an objectively significant improvement. However, technical maturity requires time. Understanding current capability boundaries and applying the right approach in the right context remains the pragmatic path to real-world deployment.