Here are the key mistakes based on expert source analysis, with practical ways to avoid them. The focus is on the most frequently occurring problems.
1. Poor prompting and overestimating agent capabilities
One of the most common mistakes is assuming that an agent will „figure out” tasks without detailed instructions, which leads to errors, hallucinations, and inefficient reasoning.
How to avoid: Write detailed system prompts that clearly define the role, goals, examples, steps (e.g., plan-act-reflect methodology), and constraints. Implement iterative testing with diverse use cases to refine the prompt’s effectiveness. Consider creating prompt templates for different scenarios that can be reused and improved over time.
2. Tool overload for a single Agent
Creating a „super agent” with numerous tools often results in confusion, slow responses, and errors in tool selection. When agents have too many capabilities, they often struggle to determine which ones are most appropriate for a given task.
How to avoid: Implement a multi-agent architecture where specialized agents handle specific subtasks. Modularize your system, define clear handover protocols between agents, and limit each agent to only the essential tools needed for its designated function. This approach creates a more maintainable and effective system where each component has clear responsibilities.
3. Over-engineering and unnecessary complexity
Building overly complicated systems for simple tasks makes them fragile, difficult to debug, and costly to maintain. Many projects fail because they implement agent-based solutions when simpler approaches would suffice.
How to avoid: Start with simplicity – evaluate whether an agent is truly necessary before proceeding (sometimes no-code automation is sufficient). Respect the complexity thresholds of your model and implement structured reasoning approaches such as chain-of-thought when appropriate. Follow the principle of progressive enhancement, adding complexity only when simpler solutions prove inadequate.
4. Improper tool implementation and lack of guardrails
Poorly described or named tools, excessive permissions, or lack of validation can lead to security vulnerabilities and inefficiencies. Agents may misuse tools or make dangerous decisions without proper constraints.
How to avoid: Document tools thoroughly, including clear names, parameter descriptions, and usage examples. Implement comprehensive guardrails such as access limitations, action confirmations, observability mechanisms, and fail-safe procedures. Consider implementing a permission model where potentially dangerous operations require explicit approval or verification steps.
5. Insufficient testing, monitoring, and iteration
A „launch and leave” approach without continuous feedback, testing, and adaptation to changes often results in degrading performance over time. Many agent systems fail because they aren’t properly maintained after initial deployment.
How to avoid: Build observability into your system by tracking decision-making processes. Implement rigorous testing protocols including golden tasks and batch testing. Establish human feedback loops for quality assurance, and create CI/CD pipelines for seamless updates. Regular performance reviews and adaptation to changing requirements are essential for long-term success.
Additional Tips
Index context effectively using retrieval-augmented generation (RAG), optimize costs by using smaller models for simpler tasks, and design user experiences that balance autonomy with appropriate escalation paths. Always begin with clear success metrics to guide your development process and evaluate performance objectively.