# How to Build Execution Layers That Don't Burn Out

Tanmay Tiwari | Agents in Production 2025 | 10:07

Source: https://www.youtube.com/watch?v=Rssz1jEC_XU
Channel: MLOps Community, now AAIF Live (https://www.youtube.com/@AAIFLive-x1r). Summarised by MLOps Talks.
Page: https://mlopstalks.com/talks/how-to-build-execution-layers-that-dont-burn-out
Published: 2025-09-29
Tags: agents, guardrails, memory, planning

## TL;DR
- An execution layer should keep the task's main intention in view instead of reacting separately to every input.
- Agent autonomy means taking responsibility for solving a task while respecting limits such as confidentiality and safety.
- A useful system stores only the information needed for the task, decides what to do next, and stops when the work is complete.

## Summary
Tanmay Tiwari describes an execution layer for agentic systems that can keep working without constant supervision. He argues that systems often fail after the initial demo because they react to each input, lose the original intention, accumulate too much memory, and spend too long thinking. His design focuses on precision, task-specific memory, a classifier, and a clear sequence of actions. The system can draft emails, update sheets, publish material, record activity, and send documents. Tanmay also shows examples of localized advertising and real estate messaging, where the same task is expressed differently for different audiences. The system monitors its own work and stops when it has completed the requested task. He treats autonomy as responsibility rather than unlimited freedom, with guardrails around confidential or illegal information. The talk is highly visual and example-driven, with less detail about implementation than about the behavior the execution layer should produce.

## Key ideas
### Execution systems lose their purpose when they react to inputs one at a time
[00:27](https://www.youtube.com/watch?v=Rssz1jEC_XU&t=27s)
Tanmay says systems can look impressive at first because they respond quickly, then fail as the task becomes more complex. With multiple APIs, orchestration breaks when the system no longer carries the main intention through its actions. He also argues that memory should not become a complete journal. A generative system needs to keep the small amount of information that helps it decide what to do next. The AI should decide what is worth storing instead of saving everything automatically.

### Agent autonomy is bounded responsibility
[02:20](https://www.youtube.com/watch?v=Rssz1jEC_XU&t=140s)
Tanmay separates autonomy from unrestricted freedom. The system receives a task and tries to solve it, but it should still refuse to provide confidential or illegal information. He describes this as responsibility: the agent is accountable for moving the task forward while operating within limits. The execution layer must also decide when to stick with a plan, what information to retain, and when further thought has become wasteful. Its purpose is to complete work rather than continue generating activity.

### The execution layer values precision over personality
[03:13](https://www.youtube.com/watch?v=Rssz1jEC_XU&t=193s)
Tanmay says language models do not behave like exact arithmetic because the right path depends on the person, situation, and goal. Someone asking how to earn a million dollars may need a different answer depending on what they already know. The system should loop less, remember what matters, and focus on the client's task. He describes it as a quiet operator that works in the background and follows the instructions it has been given.

### A simple pipeline can force the model to think about action
[04:00](https://www.youtube.com/watch?v=Rssz1jEC_XU&t=240s)
Tanmay presents a sequence built around input, trigger, and task classification. He says this simple-looking structure is important because the model has to pass through it before acting. In his advertising example, the system creates a concrete plan: draft an email, fix a sheet, publish the result, record what happened, and send documents. The emphasis is on shipping work rather than producing an elaborate plan without execution.

### The system monitors outcomes and keeps only useful feedback
[04:48](https://www.youtube.com/watch?v=Rssz1jEC_XU&t=288s)
Tanmay's execution layer is meant to show what has already happened while checking whether the work is effective. In the examples, it can use screenshots of operations, WhatsApp conversations, email replies, Shopify activity, and financial alerts. The system does not expose every internal instruction. It presents the outcome and retains information that helps it sell the product or finish the task. When the task is complete, it stops instead of inventing more work.

### One task can require different messages for different audiences
[06:57](https://www.youtube.com/watch?v=Rssz1jEC_XU&t=417s)
Tanmay demonstrates mobile phone advertising for audiences in India, New York, Germany, and Britain. The product and task stay the same, while tone and emphasis change by region. He gives examples such as a more swagger-oriented style in India, a more direct commercial emphasis for another audience, and a subtler British voice. He makes the same point with commercial real estate, where the system changes its message while keeping the underlying sales goal in view.

### The system should treat visible progress as part of its objective
[08:42](https://www.youtube.com/watch?v=Rssz1jEC_XU&t=522s)
Tanmay says an AI system can interpret procrastination as a failure to make progress. If it believes the user is doing nothing, it may help more actively. This behavior has to be prompted deliberately because the system is quick to please and may otherwise accept activity that does not advance the task. His final point is that the execution layer should be judged by whether it moves the work forward, not by whether it produces an entertaining conversation.

## Notable quotes
- "Autonomy doesn't mean it's all freedom. It's about responsibility." (02:20)
- "The execution layer is more about precision over personality." (03:13)
- "It is operating but it is following what it has been told and trying to solve the client's issue." (03:59)
- "What does AI consider evil? Procrastination." (09:06)

## Tools & references mentioned
- ChatGPT
- Llama
- Shopify
- BMW M5

## Who should watch
- You are building an agent that works across several APIs and keeps losing the original task.
- Your system stores long histories but still spends too much time thinking instead of completing work.
- You want examples of task-focused agents that adapt messages to different audiences and stop after the job is done.

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