OnepieceKing2222

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.

I’m working on an internal AI workflow where an XML 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.

No chatgpt or Claude answers

I’m working on an enterprise GenAI/RAG application and looking into how teams are implementing guardrails in production, especially when the architecture may involve both Azure and AWS.

I’m interested in real-world approaches for:

\- Prompt/input validation and jailbreak detection

\- PII and sensitive-data protection

\- Prompt injection protection for RAG

\- Output/content filtering

\- Hallucination/grounding checks

\- Tool/agent access restrictions

\- Token, cost, and rate-limit controls

\- Logging, monitoring, and auditability

\- Fail-safe/fallback behavior when a guardrail blocks a request

For those using Azure AI Foundry / Azure AI Content Safety or AWS Bedrock Guardrails, how are you integrating them into your production architecture?

A few things I’d especially like to understand:

1. Do you put guardrails before and after the LLM, or at multiple points in the RAG/agent pipeline?

2. Are you relying primarily on the cloud-provider guardrails, or building an additional application-level layer?

3. How do you handle guardrails consistently when the application uses both Azure and AWS?

4. What has worked well in production, and what initially looked good but caused latency, false positives, or other problems?

Would appreciate examples of production architectures or lessons learned.

NOTE: No chatgpt and Claude answers.

Need honest production grade solution

I’m working on an enterprise GenAI/RAG application and looking into how teams are implementing guardrails in production, especially when the architecture may involve both Azure and AWS.

I’m interested in real-world approaches for:

- Prompt/input validation and jailbreak detection

- PII and sensitive-data protection

- Prompt injection protection for RAG

- Output/content filtering

- Hallucination/grounding checks

- Tool/agent access restrictions

- Token, cost, and rate-limit controls

- Logging, monitoring, and auditability

- Fail-safe/fallback behavior when a guardrail blocks a request

For those using Azure AI Foundry / Azure AI Content Safety or AWS Bedrock Guardrails, how are you integrating them into your production architecture?

A few things I’d especially like to understand:

1. Do you put guardrails before and after the LLM, or at multiple points in the RAG/agent pipeline?

2. Are you relying primarily on the cloud-provider guardrails, or building an additional application-level layer?

3. How do you handle guardrails consistently when the application uses both Azure and AWS?

4. What has worked well in production, and what initially looked good but caused latency, false positives, or other problems?

Would appreciate examples of production architectures or lessons learned.

NOTE: No chatgpt and Claude answers.

Need honest production grade solution

Is there any way in the SQL way directly or the tools for that?