AI Agent development is not a one-size-fits-all architectural design, but rather a process that evolves gradually from the simplest API calls based on actual requirements. Key principles include: don’t use Agents for problems that can be solved with a single API, multi-step doesn’t necessarily require Agents, and only introduce conversational Agents when user participation and feature complexity are needed. The development process goes through stages of tool expansion, context loss of control, and memory system introduction, each with its specific technical challenges and solutions.
AI Agent development has become a hot topic in recent years, but many developers frequently encounter pitfalls during actual construction. The distance from theory to practice is often farther than imagined.
video source: “https://www.youtube.com/watch?v=FwOTs4UxQS4”
From API to Agent: Requirements-Driven Evolution Logic
Phase 1: Applicable Scenarios for Single API Calls
The first mistake many developers make is using Agents just for the sake of using Agents. In reality, a large number of AI application scenarios can be perfectly solved with simple API calls.
Take content creation as an example – generating titles, designing visual elements, and other tasks are essentially “input content, output results” single interactions. These tasks have several obvious characteristics: clear requirements, definite output, no need for intermediate intervention. In such cases, introducing MCP AI Agents architecture is not only technical over-engineering but also brings unnecessary complexity and costs, similar to introducing GPU resource management.
Criteria for Single API Applicability
- Clear and singular task objectives
- Definite input-output relationships
- No need for user participation midway
- Satisfactory results achievable in one attempt
Phase 2: The Confusion Between Workflow vs Agent Selection
When tasks become complex and require multiple steps to complete, many developers intuitively think they need Agents. But there’s an important distinction here: multi-step tasks don’t necessarily require Agents.
Take automatic video editing as an example – the entire process includes: video to subtitles, analyzing filler words, generating editing plans, executing editing operations. Although there are many steps, each link is deterministic and doesn’t require user intervention. In such scenarios, AI workflow automation with Workflow chain structures is more suitable than Agents.
Key Characteristics for Workflow Applicability
- Fixed and predictable steps
- No user participation needed in intermediate processes
- Definite input, one-time output delivery
- Clear beginning and end to the process
Two Real Signals for Needing Agents
Signal 1: Processes That Must Involve Users
When task results cannot achieve user satisfaction in one attempt and require repeated adjustments and optimization, the value of conversational Agents truly emerges. At this point, you can refer to the ChatGPT Agents Development Guide. Special effects generation is a typical example – few users are completely satisfied with first-generation results and usually need multiple rounds of adjustments in style, rhythm, details, etc.
The characteristic of such tasks is that result evaluation is subjective, or model capabilities have limitations, requiring human guidance and feedback. Traditional button-based interactions in such scenarios would lead to exponential growth in interface complexity.
Signal 2: Exponential Growth of Feature Options
When product features are so rich that specialized front-end interfaces need to be designed for every type of need, conversational Agents become the best choice as a universal entry point. This avoids the product interface becoming an “airplane cockpit” dilemma.
Indicators for Needing Agents
- Users need to participate repeatedly in intermediate processes
- Task results have subjective judgment nature
- Too many feature options for front-end to handle
- Need for a universal interaction entry point
Technology Selection: Running First Matters More Than Perfection
Avoiding Architectural Over-Design Traps
Many developers, after deciding to use Agents, immediately seek the most powerful and complete frameworks. This thinking seems reasonable but is conceptually wrong. Complex architectures induce developers to start designing nodes and processes without validating basic functionality, which is equivalent to working behind closed doors, similar to common problems in MLOps system architecture.
The long-chain execution mode of conversational Agents is fundamentally different from Workflows. Workflows need to run from start to finish, so they need to consider complex issues like task distribution, retries, and queue scheduling, similar to challenges in elastic distributed training. But the long chains of conversational Agents can be segmented – they can stop after executing a segment to interact with users, greatly reducing requirements for backend scheduling systems.
Pragmatic Technology Choices
Choose solutions with high integration and quick start-up, such as AI SDK. While they may not be as feature-rich as certain frameworks, their core advantage is the ability to quickly get systems running. As long as basic conversation and tool calling can be completed, iterative validation can be performed in real tasks.
System Prompts: Progressive Optimization Starting Simple
Avoid Pursuing Perfection from the Start
Many developers collect various “explosive effect” system prompts, hoping to achieve optimal results in one step. But this approach often backfires, not only failing to improve results but also causing token consumption to explode.
Complex prompts make models start breaking down steps and planning processes, turning originally simple tasks into complex ones. More importantly, these generic prompts may not suit specific application scenarios.
Progressive Prompt Optimization Strategy
The correct approach is to start with the most basic role definitions, observe the model’s behavioral patterns, then gradually add constraints and optimization instructions based on actual needs. This method ensures every change has a clear purpose and verifiable effect.
Staged Principles for Prompt Optimization
- Keep the first version concise, avoid too many restrictions
- Observe the model’s natural behavioral patterns
- Gradually add constraints based on actual problems
- Every modification should have clear verification standards
Tool Expansion and Context Loss of Control Critical Points
Capability Leaps from Tools
When basic prompt optimization cannot solve capability deficiencies, introducing tools becomes inevitable. Adding tools brings obvious capability leaps, making Agents truly start showing intelligent behavior.
Each tool addition makes the system noticeably smarter – previously impossible tasks become feasible, and barely doable things become smooth. The development experience at this stage is usually very positive, easily creating the illusion that “more tools are better.”
Hidden Crisis of Context Pollution
But when the number of tools exceeds a certain critical point, system performance shows continuous decline. This isn’t a model capability issue but attention dispersion caused by context loss of control. Each tool brings extensive documentation, plus task input, conversation history, and other information – the model’s attention gets evenly dispersed, leading to overall performance decline.
Typical Symptoms of Context Loss of Control
- Continuously declining success rates
- Accuracy fluctuating high and low
- Models starting to “not understand” instructions
- Increasingly chaotic execution processes
Context Engineering: Precise Attention Management
Task-Oriented Context Isolation
When context loss of control problems appear, Context Engineering becomes necessary. Its core idea is to let models only see relevant information when executing specific tasks, avoiding interference from irrelevant information.
Taking video Agents as an example, design tasks and code implementation are two completely different cognitive modes. Design requires open thinking, focusing on abstract concepts like style and atmosphere; code implementation requires precise thinking, focusing on specific specifications like interfaces and formats. Mixing these two types of information causes mutual pollution, affecting overall effectiveness.
Introduction Timing for Sub-Agent Architecture
Sub-Agent architecture only makes sense when different tasks clearly need different contexts. This usually manifests as needing a top-level planner responsible for global coordination and multiple specialized executors each handling their duties. But the key is that these Sub-Agents must only see information they need, otherwise the isolation purpose is lost.
Memory Systems: From Optimization Item to Necessity
Cost Issues of Information Transfer
After introducing Sub-Agent architecture, information transfer problems are immediately faced. If planners need to completely transfer user-input code to executors, two serious problems arise: first, paying for copy-paste with high output token costs; second, inability to guarantee information integrity as models might “helpfully” modify content.
Pointer-Style Information Management
The core of memory systems is using pointers instead of content transfer. Planners store content in file systems and only pass file paths to executors, who read required content based on paths. This approach both reduces token costs and ensures information accuracy.
Memory System Classification Logic
- Memory: Information that disappears after single conversation rounds end
- Storage: State information that needs cross-round preservation
- Selection criteria: Information lifecycle and usage scope
Industry Perspective
From this development journey, we can see that AI Agent design philosophy fundamentally differs from traditional software engineering. Traditional software pursues determinism and predictability, so comprehensive upfront architectural design is beneficial. But AI Agents are inherently non-deterministic systems – layering complex architecture on top equals adding uncertainty upon uncertainty.
This difference reflects an important characteristic of AI-native applications: requirements-driven evolutionary development. Rather than designing perfect architecture from the start, it’s better to begin with the simplest solutions and evolve gradually based on actual problems. This not only reduces development risks but also ensures every architectural decision has clear business value.
From an industry development perspective, current AI Agent frameworks and tutorials often showcase “graduation project” level complete architectures but lack descriptions of evolutionary processes. This causes many developers to frequently encounter pitfalls in practice, unable to understand the real value of each architectural component.
Future AI Agent development tools and platforms should place more emphasis on supporting progressive development, providing smooth upgrade paths from simple to complex, rather than requiring developers to master all concepts from the start.
Conclusion
AI Agent development is a requirements-driven evolutionary process, not a one-time architectural design. Starting from single API calls, going through Workflow selection, Agent introduction, tool expansion, context management, memory systems, and other stages, each stage has its specific technical challenges and applicable scenarios.
The key is resisting over-design temptation and always being problem-solving oriented. Don’t use advanced technology just to use it – introduce corresponding architectural components only when truly needed. This pragmatic development approach not only reduces development costs and risks but also ensures final system practicality and maintainability.
For teams currently in or preparing to enter AI Agent development, understanding this evolutionary logic is more important than mastering any specific framework. Because frameworks will change, but problem essence and solution approaches are relatively stable.
FAQ
Q: When should you choose Workflow over Agent? A: When task steps are fixed, don’t require user participation midway, and input-output relationships are definite, choose Workflow. Typical examples include data processing pipelines and automated report generation.
Q: How to determine if Sub-Agent architecture is needed? A: When different tasks clearly need different types of context information that would interfere with each other, Sub-Agents are needed. The key criterion is context isolation requirements, not task complexity.
Q: How should memory system memory and storage be chosen? A: Based on information lifecycle. Temporary information used only in current conversations goes to memory; state information needing cross-conversation preservation goes to storage. Avoid storing short-term information in storage causing pollution.
Q: How many tools are too many? A: There’s no fixed number – key is system performance. When success rates start continuously declining and models begin frequent comprehension errors, tool quantity has exceeded context capacity.
Q: How to avoid over-design traps? A: Always start with the simplest solutions, only introducing corresponding solutions when encountering specific problems. Every architectural decision should have clear business drivers, not for technological advancement.
