Troubleshooting: All my agents are returning 'I don't know' on clear tasks.

That "I don't know" loop is so frustrating, especially on clear tasks! Your hunch about role/goal setup is spot on - that's usually culprit number one.

First thing I always check is the agent's goal description. It needs to be incredibly specific and action-oriented. "Summarize articles" is too vague. Try "You are a research assistant who condenses web articles into three bullet points highlighting key claims." That gives the LLM a concrete job description.

Next, peek at your task instructions. Are they directly tied to the agent's role? The task description should read like a natural next step for that specific agent to execute their goal. If there's a mismatch, the agent gets confused and defaults to "I don't know."

Specific and action-oriented is good advice, but it's only half the battle. You can have the perfect agent goal and still get "I don't know" if your task's expected output isn't set correctly.

People miss that the "I don't know" is often the agent's literal answer to a vague task. If your expected output is just "results" or "answer," the agent has no concrete target. It decides it doesn't know what "results" should look like. You need to define the output format in the task itself, not just hope the agent's goal covers it.

That's where the real lock-in starts, by the way. You're now writing ultra-specific prompts to make the tool work, which are useless if you ever switch platforms.

Spot on about the output format. I've seen agents choke because the task expected a "summary" but the agent's internal prompt template appended "answer in a friendly tone," causing a format mismatch the LLM couldn't resolve.

But you're wrong about the lock-in. The real problem is when you have to start pre-engineering context windows because the agent system silently prepends a novel's worth of boilerplate to every call. Your "ultra-specific prompts" are the least of it. You're locked into the framework's entire orchestration overhead, which is what really kills portability.

The fix is to log the exact raw prompt sent to the LLM. Nine times out of ten, the task description you wrote isn't the one being used.

You're focusing on the right area, but the first diagnostic step is often simpler. You said you double-checked the LLM config, but have you verified the model's capability itself? An agent system built on a weak or poorly instructed base model will default to "I don't know" regardless of your goal descriptions.

Before you rewrite all your prompts, make a direct API call to your configured OpenAI model with a bare-bones version of your "summarize this article" task. If it returns a coherent summary, then you can isolate the issue to CrewAI's prompt wrapping or agent logic. If it also returns "I don't know," your issue is foundational and you need to adjust the base model or its system instructions outside the framework. This cost you one API call and saves hours of tuning agent roles.

That's a really good point about logging the raw prompt. I hadn't thought to check there. When you say the framework prepends a "novel's worth of boilerplate," are you talking about the system prompt for the agent's role? Is that where the extra context is coming from that eats up the window?

The idea of lock-in from orchestration overhead makes a lot of sense. If you have to start designing tasks around the framework's internal chatter instead of just the core job, that's a huge red flag for me. How do you even begin to log that raw call in CrewAI? Is there a setting, or do you need a workaround?

First thing I check is the verbosity level on the agent itself. If it's set too low (like "low" or "minimal"), it sometimes seems to shortcut straight to "I don't know" instead of reasoning through the task. Try bumping it to "medium" just as a quick test.

But honestly, nine times out of ten it's the goal and the task description not lining up perfectly. If your agent's goal is "summarize text" but your task says "provide a summary," the agent might see that as a different action and bail. They need to be synonyms. I rewrite the task description to literally repeat a key verb from the agent's goal.

Logging the raw prompt is the single most effective debugging step, but you're correct that CrewAI doesn't expose it directly. The "novel's worth of boilerplate" isn't just the agent's role system prompt, it's the entire orchestration logic-the task descriptions, the context of other agents, the memory instructions-all concatenated before your actual query.

The workaround is to use the LLM's native logging or a callback. For OpenAI, you can enable logging in your account or, more practically, implement a simple `CallbackHandler` for the CrewAI process that intercepts and prints the `raw` property from the LLM call. It's a few lines but reveals everything.

This exposes the real lock-in: you're optimizing for the framework's hidden prompt engineering, not the task logic. If you see a 500-token preamble before your three-sentence task, that's your overhead.

That loop is maddening, I've hit it too. Everyone's advice here about checking the goal and task alignment is spot on.

But the logging tip from user413 and user648 is a game changer I haven't tried. If CrewAI is wrapping my simple "summarize this" prompt into a huge internal one, no wonder the agent gets confused. Makes me wonder, is the "I don't know" sometimes just the LLM hitting a context limit on that hidden prompt and giving up?

Going to try that direct API call test first, though. Easy sanity check. If that works, then I'll have to figure out how to log the raw call like they said.

Your hunch about the agent role/goal setup is statistically likely, but you need to validate it before spending hours rewriting prompts. The first thing I'd do is what user648 suggested, but with a critical refinement: make that direct API call to OpenAI, but replicate *exactly* the system prompt that CrewAI is likely injecting for the agent's role. Don't just use a bare-bones task.

You can often find the default template in the CrewAI source or documentation. If your agent's role is "Researcher," the system prompt might be something overly verbose like "You are an AI Research Assistant designed to..." Your test should use that full boilerplate, not just your clean task instruction. This isolates whether the failure is in the base instruction layer the framework applies, which is a common failure point that prompt-tuning within the framework won't fix.

If the direct call with the full inferred system prompt works, then the issue is likely in the task-to-agent handoff or context assembly, and you must move to logging the raw payload as others described.

You're absolutely right that the agent's goal needs to be specific, but I'd push it one step further. The goal shouldn't just describe the agent's job; it should *constrain its output format*. I've had success embedding the format right in the goal, like "You are a research assistant whose outputs are always exactly three bullet points, each starting with a bolded claim." It primes the LLM before the task even arrives.

Also, that "natural next step" alignment is tricky. I've seen tasks that *seem* aligned still fail because the agent's internal reasoning template adds unexpected framing. The agent might think "My goal is to condense, but this task says 'provide'... is that outside my scope?" 😅 Explicitly using the same key verb in both the goal and task is a solid, simple fix.

Oh wow, this is a lightbulb moment for me. So it's not just about the agent's job description, but literally telling it *how* to format the answer in the task? That explains a lot.

I've been trying to get a simple email list segmentation task to run, and my expected output was just "list of segments." No wonder it's confused.

But then, if I have to define "a JSON array with the keys 'segment_name' and 'criteria'" in every single task, that's... a lot. That's the lock-in you're talking about, right? If I switch tools, I'd have to rewrite all of that specific instruction.

That's a classic starting pain point, and your hunch about role/goal alignment is often correct. Before you rewrite all your prompts, I'd suggest a quick sanity check on the task's `expected_output` field. If it's too vague or missing, sometimes the agent just shuts down.

Try making it hyper-specific, like "A three-sentence summary of the article's main argument." It sounds silly, but I've seen agents go from "I don't know" to perfect output just from that nudge. It gives the LLM a concrete template to fill.

> if CrewAI is wrapping my simple "summarize this" prompt into a huge internal one, no wonder the agent gets confused.

Exactly. That direct API test is your baseline. If a clean call works, you know the core LLM can do the job. The problem is in the wrapper.

But I'd skip trying to perfectly reconstruct CrewAI's internal system prompt for that test. It's a moving target. Instead, log a single real call from your broken agent flow using the callback workaround. You'll see the exact, monstrous prompt it's choking on.

Your hunch about hitting a context limit is probably right. I've seen the framework pack so much "orchestration narrative" into the prompt that the actual task gets truncated or buried. The agent isn't confused - it literally can't see the real question.

Your hunch is correct. When I see that behavior, my immediate diagnostic is to bypass CrewAI's orchestration entirely and test the LLM directly with a clean system prompt and your exact task. This isolates whether the core model is functional or if the framework's prompt wrapping is causing the failure.

While everyone's advice on goal/task alignment is valid, I find the most common root cause is actually the hidden prompt inflation user1185 mentioned. That "I don't know" often translates to the LLM hitting a token limit on a convoluted internal prompt, or the agent's actual instruction being buried under paragraphs of framework boilerplate.

Implement a simple callback to log one raw prompt sent to the API. You'll likely find your "summarize this article" is preceded by 400 tokens of role-playing context and memory instructions the agent can't parse.