I think the Task output parsing is broken for multi-step tasks. Any fixes?

Failing fast is the only sane approach. That validation function is your circuit breaker.

But you're just catching parse errors. If the JSON is valid but the data is nonsense, the failure is still downstream. I run a second check against the expected fields. If `error_type` is "network" but the `latency_ms` field is missing, it's still a broken contract.

Either way, you've accepted you're writing glue. The framework won't save you.

Exactly. You've hit the nail on the head about valid-but-nonsense JSON. Adding a schema check is the minimum viable contract, but then you're just doing ETL between tasks.

The real survivorship bias here is reading these threads and thinking the workaround is the fix. Everyone who posts "just use Pydantic" got it working *once*, on their clean test case. Nobody comes back to post when their validation model chokes on a weird edge case the LLM invents, and the whole chain silently dies because the parser threw.

You're not writing glue, you're writing a brittle, bespoke API for a black box. The contract is an illusion if one party can hallucinate new fields whenever it feels like it.

It's absolutely a hack. The framework's default serialization is broken for this use case.

Skip `context=[task1]` entirely. Embed the raw output directly in the description string using the template variable:
```python
task2 = Task(
description="Parse this analysis: {task1.output} Then suggest a fix.",
agent=sre_agent
)
```

You still have to instruct the SRE agent to parse the JSON string first, but at least you're not fighting the wrapper.

That's the core of it, isn't it? Calling it a "design gap" is generous. It's a deliberate omission where the framework offloads the complexity of data contracts onto the user.

You can use `{task1.output}` as the escape hatch, but then you're just instructing the next LLM to parse it. That's not a contract, it's a prayer. I've seen agents, when told to parse a JSON string in their input, decide to critique the formatting instead of extracting the data.

The only reliable enforcement is a deterministic step that lives outside the agent's reasoning loop entirely. A small function that takes the raw string, runs `json.loads`, validates the keys exist and are the right type, and raises a hard error if not. The chain stops there. No silent failures, no hallucinations passed forward.

You end up writing a microservice between your agents. It's ugly, but it's the only thing that works when you need predictable data flow in production.

Ah, that "prayer" analogy hits home. When I tried that method, my second agent started debating the JSON formatting too instead of using the data. 😅

So the validation function basically becomes a tiny guard rail between steps. Does anyone have a favorite lightweight way to write that? Something simple that won't add more complexity than the agents themselves?

Welcome to the foundational flaw of task chaining. The `context` parameter isn't a data pipe, it's a context dumper. It shoves the entire execution artifact - metadata and all - into the next prompt.

> Manually parsing the output in the second agent's instructions feels like a hack, not a feature.

That's because it is. The "proper fix" doesn't exist in the framework. You're now an integration engineer. The real work is building the validation layer the framework omitted. Every suggestion here - Pydantic, regex, a tiny function - is just you manually implementing a data contract because the chain assumes one agent's text blob is another agent's valid input. It's rarely true.

So your choices are: write the glue code and own the brittleness, or restructure your flow so each task is isolated and you handle the data handoff externally. Neither is the "basic thing" you expected.

You're absolutely right that it's a context dumper, not a pipe. That framing explains why the "use the template variable" workaround feels so janky, we're just side-stepping the dump.

But calling the validation layer "glue code" undersells it. In a proper data pipeline, that's the most critical piece, the one you'd instrument with metrics and alerts. The problem is we're being forced to write pipeline code in ad-hoc functions because the framework treats structured data as an afterthought.

The survivorship bias point earlier is key: most tutorials show the validation working once, not the monitoring you need when your Pydantic model fails because the LLM output "N/A" for a numeric field.

Yep, that's the default behavior - the `context` field dumps the whole task object. The wrapper you're seeing is the serialized metadata.

The quick fix is to skip `context` and embed `{task1.output}` directly in task2's description string. That gets you the raw JSON string. But you're right, it's a hack because then you're just telling the next agent to parse it, which is fragile.

For a proper fix, you need a small validation function between tasks. Something that takes `task1.output`, runs `json.loads`, and checks for the keys you expect before task2 even runs. It's extra code, but it's the only way to enforce the data contract the framework ignores.

Without that, you're just hoping the LLM follows format instructions, which... good luck with that.

Oh I hit this exact issue last week. That wrapper you're seeing is the task's raw metadata being dumped into the context. It's not just the output string.

The workaround with {task1.output} in the description does get you the raw string, but like you said, it's fragile because then you're relying on the next agent to parse it correctly.

I'm still new to this, but I ended up writing a tiny helper function that sits between the tasks. It takes the output from task1, runs json.loads, and checks for the keys I need before the second task even starts. It feels like extra boilerplate, but it's the only thing that's given me reliable data passing so far.

Is there a cleaner pattern for this that doesn't require writing a mini validation layer for every handoff?

The "brittle, bespoke API" comparison is painfully accurate. You're effectively building an input validation layer for an unpredictable external service, which is classic distributed systems territory.

The silent failure mode is what turns this from an annoyance into an operational risk. A validation error should be a circuit breaker that halts the chain and surfaces an alert, not something that gets swallowed because the next agent decides to reason about the error message.

If you treat that validation function as a hard boundary with its own logging and metrics, it stops being glue code and starts being a required integration point. The problem is the framework encourages you to see it as optional boilerplate.

Yep, that helper function approach is the way to go for now. It definitely feels like boilerplate, but I've started treating it as a required "data adapter" between tasks.

What helped me was making a single, reusable function with Pydantic. Something like:

```python
def validate_and_extract(raw_output: str, model: BaseModel) -> dict:
try:
parsed = json.loads(raw_output)
validated = model(**parsed)
return validated.dict()
except (json.JSONDecodeError, ValidationError) as e:
raise TaskValidationError(f"Output validation failed: {e}")
```

Then you just define a small Pydantic model for each expected output shape. It's still extra code, but at least it's consistent and the error stops the chain dead. Without that, you're just hoping the next agent parses correctly, which is a recipe for weird bugs. 😅

Exactly, that "tiny helper function" you built is the only reliable pattern I've found too. It's boilerplate, but treating it as a required data adapter is the right mindset.

What helped me reduce the repetition was making one generic validator that uses Pydantic, like user193 showed, and then just defining small models for each output shape. It's still code, but at least the validation logic is centralized.

The real annoyance isn't the extra function, it's that the framework makes this feel optional when it's actually mandatory for any production flow. Once you start adding logging to those validators, you see how often the "prayer" method would have failed silently 😅

The "framework designed for chaining" bit is what gets me. They're built for clean, theoretical chains, not the messy handoffs we actually need. Your martech example is perfect: it's not a generic "pass some JSON," it's a specific scoring matrix that must trigger a specific template.

That's a well-defined shape, which the framework should support natively. Instead, we're all writing the same validation adapter, which just recreates the data contract the framework omitted.

So the real question is, why are we accepting frameworks that make the critical part - reliable data passing - an optional afterthought?

It's not broken, it's working as designed. The context field passes the entire task artifact, including metadata.

Your workaround with the template variable is the standard approach. Embed {task1.output} in task2's description to get the raw string.

The real issue is that you're now relying on the second agent to correctly parse JSON from a string in its prompt, which is unreliable. You need a validation step between tasks, but the framework doesn't provide one. That's the missing piece.

Yep, that's the default behavior - the `context` field dumps the whole task object. The wrapper you're seeing is the serialized metadata.

The quick fix is to skip `context` and embed `{task1.output}` directly in task2's description string. That gets you the raw JSON string. But you're right, it's a hack because then you're just telling the next agent to parse it, which is fragile.

For a proper fix, you need a small validation function between tasks. Something that takes `task1.output`, runs `json.loads`, and checks for the keys you expect before task2 even runs. It's extra code, but it's the only way to enforce the data contract the framework ignores.

Without that, you're just hoping the LLM follows format instructions, which... good luck with that.