I've been conducting extensive integration tests with SuperAGI's agent framework over the past fortnight, specifically focusing on tool selection reliability within complex workflows. A persistent pattern has emerged: despite providing what I consider to be granular, unambiguous instructions and a well-defined tool schema, the agent frequently selects an inappropriate tool, leading to workflow failure or nonsensical output loops. This isn't a case of a missing tool; it's a misalignment between the agent's interpretation of the task and the available toolset's purpose.
My hypothesis is that this is less a bug in the tool-calling mechanism and more a fundamental prompt engineering or agent configuration issue. The agent's reasoning layer appears to be prioritizing certain keywords or tool descriptions over the explicit intent outlined in the objective and task instructions. For instance, in a test designed to fetch data from a specific API endpoint and then format it, the agent will often bypass the designated `fetch_customer_data` tool and incorrectly invoke a generic `web_search` tool, even when the objective contains no search-related language.
Consider this simplified configuration snippet from my test environment:
```yaml
agent_name: "DataProcessor"
tools:
- "fetch_customer_data"
- "format_json_to_table"
- "web_search" # Included for other tasks but should not be triggered here
instructions: |
Objective: Retrieve the latest customer dataset from the internal API and convert it to a markdown table.
Constraints:
1. Use ONLY the 'fetch_customer_data' tool for the retrieval step. The required API key is set in the environment.
2. Do not perform any web searches. The data is available locally via the API.
3. Pass the result from step 1 directly to the 'format_json_to_table' tool.
```
The observed behavior, however, is that the agent's first action is often to call `web_search` with a query like "latest customer dataset internal API." This suggests the LLM backing the agent is completing a pattern based on its training, rather than strictly adhering to the provided instruction set.
I am seeking community insight on several fronts:
* Has anyone else encountered and successfully mitigated this class of tool-selection error?
* Are there specific prompting strategies within SuperAGI's context—perhaps in the agent instructions, tool descriptions, or even the goal schema—that force stricter adherence?
* Could this be related to the underlying model's temperature or reasoning configuration, suggesting a need for custom agent templates with more constrained parameters?
I will be sharing my findings on any successful middleware or prompt-layer fixes. The goal is a deterministic workflow, which is paramount for production integrations.
API first.
IntegrationWizard