Executive Summary

As teams increasingly deploy agents and copilots, the volume of intermediate tool outputs (logs, search results, function traces, scrapes) being fed back into LLMs is inflating token costs and causing brittle context-window failures; platform and devops teams running many automated agents feel this pain most acutely. The problem is both financial (higher per-request token bills) and operational (prompts hitting window limits and failing unpredictably). You could build a developer-first middleware and SDK plus optional hosted service that intercepts tool outputs and applies configurable, loss-aware summarization, filtering, semantic deduplication and schema extraction, emitting compressed payloads with provenance and fidelity metadata before models are called. Ship CI/CD hooks, observability dashboards and pluggable policies (aggressive compression, fidelity-preserving extraction, latency vs quality controls) so teams can tune the trade-offs. This is a timely market: roughly $3.6B of addressable spend (1,000,000 developer teams × $3.6K ACV) with scores of 95/100 for market fit and 88/100 for revenue potential, driven by broad LLM adoption and slowly increasing context windows that keep software-level compression necessary. Medium competition means there’s room for a focused product that integrates deeply into developer workflows. To win, prioritize low-friction SDKs, strong observability/SLAs, and domain-aware, loss-aware compression tuned for agent workflows to deliver measurable token savings (likely tens of percent) and minimal integration friction—while being upfront about challenges like model drift, latency trade-offs, and accuracy validation that you’ll need to manage.

Market Opportunity

Cut token waste: summarize and filter tool outputs before AI agents consume them targets a $3.6B = 1,000,000 developer teams × $3.6K ACV total addressable market with medium saturation and a year-over-year growth rate of 35% YoY (reflects growth in AI developer tools and autonomous agents; corroborated by industry analyst trends for AI developer tooling and RAG adoption 2023-2025).

Key trends driving demand: LLM adoption for automation — more teams are deploying agents and copilots which increases the volume of tool outputs fed to models and makes token management a material cost.; Context-window limits persist — bigger context windows are appearing slowly, so software-level compression and summarization remain necessary to avoid failures.; Shift to developer-first AI tooling — teams expect SDKs and middleware they can embed in CI/CD and platform stacks, enabling fast adoption of lightweight agent plumbing.; Cost-conscious AI procurement — as AI bills grow, finance and platform teams demand measurable savings and governance for model usage which token-optimization directly addresses..

Key competitors include LangChain, Weaviate, Glean (enterprise search).

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Cut token waste: summarize and filter tool outputs before AI agents consume them

Executive Summary

Market Validation

Market Opportunity

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