Multi-agent orchestration patterns
Coordinating multiple agents. This post digs into the topic, drawing on recent discussion from Hacker News, Reddit, and the wider community.
The conversation that prompted this post is linked in the sources. I have tried to capture the substance of the discussion rather than just summarize it, because the comments are usually where the real insight lives.
What happened
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
Why it matters
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Practical takeaways
- Model routing sends simple queries to cheap models and hard ones to expensive models.
- Fine-tuning is overused.
- The hardest part of building with LLMs is not the model call.
- Tool use turns a chat model into an agent.
- Guardrails are not optional in production.
- Evaluation is a product, not a test.
- Caching is the single biggest cost lever for LLM applications.
- Evaluation is a product, not a test.
- Context windows keep growing, but attention is not free.
- Latency is a feature.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Sources
This post is informed by discussion on langchain.com and related threads on Hacker News and Reddit. Primary source: https://langchain.com/.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.