Dependent Tool Calls Explained: Why Your AI Agent Keeps Failing

You've probably seen it before. You ask an AI agent to do something moderately complex—like "find the best-selling laptop under $1,000 on Amazon and draft a comparison email"—and it just... chokes. Or maybe it gives you a hallucinated price for a product it never actually looked up. Honestly, most people blame the "reasoning" of the model, but the real culprit is usually a mess-up in dependent tool calls.

Basically, a dependent tool call is when the output of one action is the mandatory input for the next. It’s a relay race. If the first runner (the search tool) trips, the second runner (the email drafter) has nothing to carry.

In the early days of LLMs, we just called one function at a time. Now, we’re asking models to juggle chains of logic that look more like professional software workflows than simple chat prompts. If you aren't getting these dependencies right, your agent isn't an "agent"—it's just an expensive, fancy autocomplete.

What actually makes a tool call "dependent"?

Most AI interactions are parallel. If you ask for the weather in Tokyo and London, the model can hit those two APIs at the same time. No problem. That’s a parallel tool call.

But dependent tool calls are a different beast. They require a sequence where $Step_{n+1}$ cannot exist without the data from $Step_n$.

Imagine you're building a customer support bot. The user wants to "cancel my last order." The LLM can't just call cancel_order(). It first needs to call get_user_orders(user_id). Then, it has to parse that list, find the most recent order ID, and then pass that specific ID into cancel_order(order_id).

This is where things get hairy. The model has to:

1. Understand it needs two tools.
2. Recognize it doesn't have the info for the second tool yet.
3. Wait for the first tool's JSON response.
4. Inject that response into the second call's parameters.

If the first tool returns an empty list or a weirdly formatted date, the whole chain collapses. It's a domino effect that most developers underestimate.

The "Context Bloat" problem in 2026

We're currently in a weird spot with models like Gemini 3 and GPT-5.2. They have massive context windows, but they still get "distracted" by the intermediate junk in a chain.

When you do a dependent tool call, the assistant typically sees:

• The original user prompt.
• The first tool call request.
• The first tool's raw output (often a messy JSON blob).
• The second tool call request.

By the time it gets to the third or fourth step, the model's "attention" is cluttered with raw API data it doesn't need anymore. Anthropic recently tried to solve this with something called Programmatic Tool Calling.

Instead of the LLM going back and forth with the server for every single step, the model writes a small block of Python code to handle the orchestration internally. It’s sort of like giving the AI a scratchpad to do the heavy lifting so it only reports the final result back to the main conversation. This keeps the "thought" clean.

Why your chains are breaking (and how to fix them)

Most failures in dependent tool calls happen because of "schema drift" or "parameter mismatch." You've got one tool outputting a date as MM/DD/YYYY and the next tool expecting YYYY-MM-DD. The LLM isn't a data transformation engine—at least, not a reliable one.

1. Tighten your schemas

If you’re using OpenAI’s "Strict Mode" or Gemini’s function declarations, use enums. Seriously. If Tool B depends on a "status" from Tool A, and Tool A can return "shipped," "pending," or "delivered," tell the model exactly those three options. Don't let it guess.

2. The "Orchestrator" pattern

Sometimes, the best way to handle a dependent chain isn't to let the LLM do it at all.
Instead of: LLM -> Tool 1 -> LLM -> Tool 2 -> LLM
Try: LLM -> Orchestrator Tool -> (Tool 1 + Tool 2) -> LLM

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You basically wrap the dependency in a single function on your backend. This reduces the number of "round trips" to the model, which saves you money on tokens and cuts down latency. Nobody wants to wait 12 seconds for a bot to figure out how to look up a tracking number.

3. Handle the "Null" case

What happens when the first tool returns nothing? Most prompts tell the AI what to do when things work. They rarely say: "If get_user_id returns 404, stop everything and ask the user for their email." Without this instruction, the model might try to pass "null" or "undefined" into the next tool, causing a server-side crash.

Real-world example: The Travel Agent Bot

Let’s look at a real workflow. Say you’re building a corporate travel assistant.

1. User: "Book me a room near the conference center in Austin for next Tuesday."
2. Tool 1 (Google Hotels): Returns a list of hotel IDs.
3. Tool 2 (get_hotel_availability): Requires a hotel_id from the previous list.
4. Tool 3 (apply_corporate_discount): Requires the booking_quote_id from Step 2.

In 2026, we’re seeing "agentic" frameworks like LangGraph or CrewAI handle this by creating a state machine. The "state" holds the hotel_id. If the state is empty, the graph loops back to the search phase.

It sounds complex because it is. You're basically building a distributed system where one of the nodes (the LLM) is prone to "hallucinating" that it already has the data.

The "Thinking" parameter

One of the coolest updates in recent models (specifically the Gemini 3 series) is the thinking_level parameter. In the past, we used "Chain of Thought" (CoT) prompting to make the model explain its steps. Now, the reasoning happens at the architectural level.

When a model knows it's about to perform dependent tool calls, it can use these reasoning tokens to plan the sequence before it even touches an API. It's like the model "pre-visualizing" the data flow. If it realizes it’s missing a piece of the puzzle, it can stop and ask the user for clarification instead of guessing a parameter value.

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Actionable steps for developers

If you're currently fighting with an agent that can't seem to string two actions together, here's the plan:

• Flatten the chain: If two tools are always called together, merge them into one. Don't make the LLM work harder than it has to.
• Validation layers: Write a middleware script that validates the output of Tool A before the LLM sees it. If it's garbage, fix it or throw an error immediately.
• Use "Thought Signatures": If you're on the Google AI stack, make sure you're passing back the thoughtSignature. It helps the model remember why it called the first tool when it's looking at the results for the second one.
• Small models for small tasks: Use a massive model (like GPT-4o or Gemini 1.5 Pro) to plan the sequence, but consider using a faster, smaller model to handle the actual data extraction from the tool outputs.

Dependent tool calls are the bridge between "chatting" and "doing." We're moving away from bots that just talk and toward systems that actually operate software. The more you treat your tool calls like a rigorous software pipeline rather than a casual conversation, the more reliable your AI is going to be.

Stop expecting the model to "just get it" and start defining the hand-offs with precision.

Next Steps for You:

1. Audit your current tool definitions: Look for any parameters that rely on "guessable" information and replace them with hard dependencies from previous tool outputs.
2. Implement a retry logic: Specifically for the first tool in a chain; if it fails, the whole sequence is toast, so give it a second chance before involving the LLM again.
3. Check your latency: Measure the time between the first and second tool calls—if it's over 2 seconds, consider moving that logic to a single "wrapper" function.