Build a Multi-Agent GTM Intelligence System
Brendan Short identifies that effective GTM re-engagement should be driven by specific company triggers rather than arbitrary calendar dates. The challenge lies in the fragmentation of data, where leadership changes and news announcements reside in separate records, requiring agents to scrape and parse multiple sources. Seltz solves this by providing structured people and news records in single API calls, enabling a three-agent pipeline to identify signals, enrich profiles, and generate personalized outreach efficiently. Context compaction for LLM agents aims to manage finite context windows but can inadvertently increase costs by breaking prefix caching. While standard caching rewards stable prefixes, compaction edits the history, forcing expensive cache writes instead of cheap cache reads. Five main strategies exist: truncation, rolling summarization, prompt compression, RAG-based retrieval, and KV cache eviction. Tools like LMCache address these inefficiencies by enabling non-sequential cache reuse across different serving frameworks. Lightning Fabric is a library designed to bridge the gap between the flexibility of raw PyTorch and the distributed training features of PyTorch Lightning. It allows developers to scale models to billions of parameters by making only four minor changes to existing PyTorch code, such as replacing manual device placement and loss backward calls. The tool supports advanced strategies like FSDP and DeepSpeed while maintaining control over manual training loops.
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Build a multi-agent GTM intelligence system
Brendan Short identifies that effective GTM re-engagement should be driven by specific company triggers rather than arbitrary calendar dates. The challenge lies in the fragmentation of data, where leadership changes and news announcements reside in separate records, requiring agents to scrape and parse multiple sources. Seltz solves this by providing structured people and news records in single API calls, enabling a three-agent pipeline to identify signals, enrich profiles, and generate personalized outreach efficiently.
- GTM timing should be based on company changes like new leadership or funding rounds rather than fixed nine-month intervals.
- Standard search APIs return snippets that require agents to perform expensive fetch and parse steps to extract useful data.
- Seltz provides structured records for people and news, allowing data joins to happen in a single pass without reassembling fragments.
- The multi-agent pipeline consists of a Signal Hunter, a People Enricher, and an Outreach Strategist orchestrated via CrewAI.
- The system utilizes the Model Context Protocol (MCP) to connect agents directly to the Seltz retrieval tool.
- Seltz excels at director-level data and recent news, while open web search is recommended for initial high-level executive discovery.
5 context compaction strategies for LLM agents
Context compaction for LLM agents aims to manage finite context windows but can inadvertently increase costs by breaking prefix caching. While standard caching rewards stable prefixes, compaction edits the history, forcing expensive cache writes instead of cheap cache reads. Five main strategies exist: truncation, rolling summarization, prompt compression, RAG-based retrieval, and KV cache eviction. Tools like LMCache address these inefficiencies by enabling non-sequential cache reuse across different serving frameworks.
- Prefix caching significantly reduces costs by matching identical leading spans of text, with providers like Anthropic offering up to 90% discounts.
- Compacting context by summarizing or editing history invalidates the cache, leading to higher cache write costs (1.25x base rate) compared to cache reads (0.1x base rate).
- Truncation is the simplest compaction method but results in permanent information loss of early decisions.
- Prompt compression tools like LLMLingua and LLMLingua-2 use smaller models or encoders to remove low-relevance tokens with minimal accuracy loss.
- KV cache eviction manages GPU memory by dropping or offloading tensors rather than deleting text, allowing for recomputation or retrieval from slower storage.
- LMCache is an open-source layer that enables cache reuse at any prompt position via CacheBlend, supporting vLLM, SGLang, and Dynamo.
Scale models to billions of parameters
Lightning Fabric is a library designed to bridge the gap between the flexibility of raw PyTorch and the distributed training features of PyTorch Lightning. It allows developers to scale models to billions of parameters by making only four minor changes to existing PyTorch code, such as replacing manual device placement and loss backward calls. The tool supports advanced strategies like FSDP and DeepSpeed while maintaining control over manual training loops.
- Lightning Fabric combines PyTorch's flexibility with PyTorch Lightning's distributed training capabilities.
- Scaling to billion-parameter models requires only four minor code modifications.
- Fabric automates device placement, eliminating the need for manual .to() and .cuda() calls.
- The library supports state-of-the-art distributed strategies including DDP, FSDP, and DeepSpeed.
- Fabric is compatible with various hardware accelerators including Apple Silicon, CUDA GPUs, and TPUs.
- Users can build custom training abstractions for checkpointing and logging using Fabric as a foundation.