Agent Limitations & Strategy

Agents are powerful, but they work best when you understand what they can and can't do. Like any tool, knowing their strengths and limitations helps you choose the right approach and get consistently good results.

It's important to note that all of these limitations are SOLVABLE. In this Guide, we will explain the most important things to consider when creating agents. We will teach you strategies to mitigate and solve these limitations.

How Agents actually work

Agents use large language models (like Claude or Gemini) to understand your tasks and figure out steps to complete them. This gives them incredible flexibility, but it also means they inherit the natural limitations of these AI models.

Think of an Agent as having a conversation with your apps. It reads your task, thinks about what to do, takes an action, sees the result, then decides what to do next. This step-by-step reasoning is powerful but has natural boundaries.

Agents in Incredible work with a sandboxed environment where they can run code, access data from previous actions, and coordinate multiple function calls efficiently. They maintain persistence between actions through a data storage system and can handle increasingly complex workflows.

Understanding Agent limitations

Knowing these limitations helps you structure tasks for success

Memory and context limits

While agents have context window limitations as LLM-powered systems, modern implementations include several mitigations:

Current capabilities:

• Can access conversation history and maintain context throughout tasks

• Receive periodic reminders of the original task to maintain focus

• Can reference previous results stored persistently between actions

• Can work step-by-step without losing track of overall objectives

Remaining limitations:

• May need to break down very complex multi-part tasks into smaller components

• Visual truncation of extremely large datasets to manage context effectively

• Works best when tasks are structured with clear intermediate checkpoints

Signs you might need to restructure your approach:

• Agent repeats actions it already completed (though this is heavily mitigated)

• Tasks involving hundreds of interconnected steps

• Workflows requiring perfect recall of dozens of previous decisions

Data handling capabilities

Agents on Incredible can handle significantly larger datasets than earlier versions, with sophisticated approaches to data processing:

Current capabilities:

• Can process data through analysis actions with pandas and other libraries

• Can handle large datasets by working in batches and using pagination

• Can perform complex calculations and data transformations

• Supports various data formats (JSON, CSV, Excel, etc.)

• Can coordinate multiple data operations efficiently

Limitations to consider:

• Visual truncation of very large datasets for context management (though full data remains accessible)

• Must use null-safe logic for potentially malformed data

• Works best with structured approaches to large data processing

Updated data guidelines:

• Agents can effectively handle hundreds of records when using proper batching techniques

• Complex calculations across datasets are well-supported through analysis capabilities

• Perfect data consistency is achievable through structured validation approaches

Speed and execution considerations

While agents think through each step (which takes time), execution itself can be quite efficient:

Execution strengths:

• Can run multiple function calls in a single action (up to 25)

• Sandboxed environment provides immediate code execution

• Can batch operations for maximum efficiency

• Results are immediately available for subsequent actions

Speed limitations:

• Step-by-step reasoning requires processing time between actions

• Each action involves LLM inference time

• Not suitable for real-time processing requirements

• Best for workflows where quality and accuracy matter more than pure speed

Function integration and coordination

Modern agents have sophisticated capabilities for coordinating with external systems:

Integration capabilities:

• Can call functions up to 25 times per action for efficient batching

• Can coordinate between different systems and apps

• Can combine multiple operations strategically

• Can access a wide range of app integrations through pre-defined functions

Integration constraints:

• Only one unique function type per action (though callable multiple times)

• Functions must be pre-defined in the system

• Cannot dynamically create new integrations during task execution

Task instruction autonomy

Modern agents work much more autonomously than earlier versions:

Current approach:

• Works step-by-step without stopping until tasks are complete

• Assumes users have provided necessary information upfront

• Only asks questions when absolutely necessary for task completion

• Can make reasonable inferences from context and previous actions

This means:

• Less back-and-forth clarification needed

• More comprehensive task completion in single runs

• Better handling of ambiguous or partially-specified requirements

• Improved ability to adapt approaches based on intermediate results

Writing effective task instructions

Since agents work more autonomously, your instructions can focus on outcomes rather than step-by-step processes:

Focus on outcomes and constraints

Modern task instructions should specify:

• What success looks like - Clear definition of the desired end state

• Key constraints or requirements - Important boundaries or must-haves

• Data sources and destinations - Where to find information and where to put results

• Quality standards - How to validate or verify results

Updated task instruction examples

"Set up automated onboarding for new customers from our database. Ensure all contacts are properly validated and added to HubSpot with welcome emails sent. Provide a summary of processing results and flag any issues for manual review."

"Generate a comprehensive weekly sales analysis including revenue trends, top-performing products, and key insights. Deliver to the sales team via email with executive summary and detailed breakdown."

Next steps

Understanding these enhanced capabilities helps you build more effectively:

1. Audit current tasks - Identify workflows that could benefit from modern agent capabilities

2. Redesign task instructions - Focus on outcomes rather than step-by-step processes

3. Leverage batching and coordination - Combine related operations for efficiency

4. Experiment with complexity - Try more sophisticated workflows that leverage enhanced capabilities

The current version of incredible agents are significantly more capable than earlier versions. They can handle larger datasets, maintain better context, work more autonomously, and coordinate complex workflows effectively. The key is understanding both their enhanced capabilities and remaining limitations, then structuring your tasks to leverage their strengths.

Remember: Agents are incredibly powerful when used appropriately. With enhanced data handling, better memory management, and improved autonomy, they can now tackle much more sophisticated tasks while maintaining reliability and accuracy.