r/LangChain

I’m working on an internal AI workflow where an XMP file contains source tables, columns, and metadata. We pass this information to an LLM, which generates a dataflow using an internal MCP tool and our internal dataflow notation.

The current flow is roughly:

"XMP metadata → LLM → MCP tool → Dataflow → Validation"

The issue is with failures.

If the first attempt fails validation, our current approach sends the whole original text/context again along with the failure/error information and asks the LLM to correct the dataflow.

So the retry looks something like:

"Original XMP + instructions + generated dataflow + MCP response/error → LLM retry"

This is causing significantly higher token consumption, especially when the XMP/metadata is large.

I’m looking for advice on how people handle this in production.

A few approaches I’m considering:

- Keep the original metadata outside the conversation and retrieve only the relevant portions during retry

- Send only the failed node/transformation and the validation error to the LLM

- Maintain structured agent state instead of appending the entire conversation

- Summarize/compact the previous attempt before retrying

- Have the MCP tool return a small structured error instead of the full dataflow/context

- Generate the complete dataflow once and use a targeted "repair" step for failures rather than regenerating everything

Question: What architecture/pattern have you found effective for reducing token usage in agentic workflows where the input context is large but retries only need a small portion of it?

Also interested in how you handle context management, retry state, and MCP tool outputs in production coding/data-engineering agents.