AI Agent Handoff Bug: Context Not Passed

The Problem: Missing Context in Specialist-to-Specialist Handoffs

Have you ever built an AI agent system, maybe using our midday-ai tools, and noticed that when one specialized agent hands off its work to another, something crucial seems to go missing? It’s like passing a baton in a relay race, but the next runner doesn’t get the memo about what happened before. That’s precisely the bug we’ve uncovered in the Agent.stream method. Specifically, when we’re handing off information from one specialist agent to the next, the context – and I’m talking about the results from the previous agent's tools – isn't making the journey. This is a pretty big deal because without this crucial information, the next specialist agent is essentially starting from scratch, unable to build upon the work already done. It’s a jarring experience that can completely disrupt the flow of your AI's reasoning and decision-making process. This issue arises unless you, as the developer, manually step in and configure a custom inputFilter. Without that explicit intervention, the handoff just… doesn't include the vital data. This is a stark contrast to how the system handles handoffs from an orchestrator to a specialist, where a default input filter is correctly applied to ensure relevant context, like those all-important tool outputs, makes it to the next agent in line. This inconsistency creates a confusing and often frustrating development experience, as the expected behavior isn't uniform across different handoff scenarios. It leaves developers scratching their heads, wondering why their AI agents aren’t communicating effectively and why their complex workflows are breaking down at seemingly simple transition points. The core of the problem lies in a simple oversight within the code, a missing piece that prevents the natural flow of information.

Root Cause: An Oversight in agent.ts

So, where exactly does this disconnect happen? The issue stems from a specific part of the codebase in packages/agents/src/agent.ts, right in the heart of the logic that manages specialist-to-specialist handoffs. The code does check if a custom inputFilter has been provided by the developer. It looks for configuredHandoff.config.inputFilter and, if it finds one, it uses it. This is great for those who need highly customized data filtering. However, and this is the critical flaw, there’s no fallback mechanism – no else block – to handle situations where a developer hasn’t specified a custom filter. When this happens, the code essentially shrugs and moves on, completely skipping the process of filtering and preparing the context. The consequence? The target specialist agent receives the conversation history, yes, but it’s a history that’s conspicuously missing the vital toolResults generated by the agent that just finished its task. Imagine you’re a chef, and the previous chef prepared a complex sauce, but they forgot to tell you what was in it or even give you the sauce itself. You can’t possibly create the next course properly without that key ingredient. This is the same predicament the specialist agent finds itself in. The system is designed to be modular and to allow specialists to collaborate, but this missing piece of logic prevents that collaboration from being as seamless and effective as it could be. The default behavior, which should be to include tool results, is absent, forcing developers to either write custom filters for every handoff or live with incomplete context, hindering the potential of sophisticated AI agent interactions. It’s a prime example of how a small omission in code can have a significant impact on the overall functionality and usability of a complex system, particularly in the nuanced world of AI development where every piece of context can be critical.

Code Location: Pinpointing the Issue

To be precise about where this bug resides, you’ll need to navigate to the file packages/agents/src/agent.ts. This is the central hub where the logic for agent interactions, including these crucial handoffs, is managed. Within this file, specifically in the loop that processes the Agent.stream method, the problem becomes apparent. Let’s break down the current implementation with some pseudocode to illustrate the gap. As you can see in the snippet below, the code checks for the existence of configuredHandoff?.config?.inputFilter. If this custom filter is present, the code proceeds to use it, applying whatever custom logic has been defined to filter the data that will be passed along. This allows for fine-grained control over what information is transferred. However, and this is the crux of the problem, there’s a significant <--- MISSING ELSE BLOCK HERE. This absence means that if a developer doesn’t provide a custom filter, the code simply doesn’t execute any filtering logic related to context transfer. It doesn't even attempt to fall back on a default mechanism. Therefore, the toolResults that were painstakingly generated by the preceding specialist agent are never properly processed or injected into the conversationMessages that will be sent to the next agent. This leaves the subsequent agent operating with incomplete information, severely hampering its ability to perform its designated task effectively and efficiently. The consequence is a breakdown in the intended flow of information, undermining the collaborative capabilities of specialized AI agents. Developers are left to manually bridge this gap, which is not only an inefficient use of their time but also introduces the potential for errors and inconsistencies. The ideal scenario is for the system to gracefully handle both custom and default filtering, ensuring that context is always preserved, regardless of the specific configuration.

Proposed Fix: Embracing the Default

To resolve this critical issue and ensure that specialist agents can communicate effectively, we need to introduce a fallback mechanism. The proposed fix is straightforward yet highly effective: we need to add an else block to the existing conditional statement in packages/agents/src/agent.ts. This else block will be responsible for invoking a default filtering process whenever a custom inputFilter is not provided. By implementing createDefaultInputFilter(), we can ensure that toolResults are consistently extracted and then seamlessly injected into the conversation history. This will maintain the continuity of information and allow the subsequent agent to operate with the full context it needs to succeed. Imagine our previous analogy: if the sauce isn't handed over, the new chef needs a basic recipe for a standard sauce to at least keep things moving. That’s what this default filter does. It provides a sensible, out-of-the-box behavior that preserves the essential data. The suggested change involves adding the else block as shown in the provided code snippet. Inside this block, we’ll instantiate the defaultFilter using createDefaultInputFilter(). Crucially, we then construct the handoffInputData object, making sure to include the captured toolResults within the newItems array. This ensures that the data generated by the previous agent is explicitly passed. Following this, we apply the defaultFilter to this handoffInputData. The conversationMessages are then reset and repopulated with the filteredData.inputHistory. This entire process guarantees that the toolResults are correctly processed and added to the conversation, making them available to the next specialist agent. By adopting this simple yet powerful fix, we eliminate the inconsistency and ensure that context is preserved, paving the way for more robust and reliable AI agent interactions. This not only simplifies development but also unlocks the full potential of multi-agent systems, allowing them to tackle more complex problems through seamless collaboration.

For further reading on AI agent development and best practices, you can explore resources from OpenAI.