Agentified provides a context hydration platform that dynamically assembles only the relevant tools, messages, and memories for each AI agent session, cutting token usage by over 85% and reducing inference costs. Its fluent builder API, available in TypeScript and Python, lets developers register toolsets once and rely on a Context Intelligence Layer to store, select, and learn from entities, preferences, and procedural knowledge, ensuring precise tool selection and governance across any agent framework.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents that integrate many tools quickly exceed their context windows, leading to token overload, incorrect tool selection, and unreliable behavior. Developers repeatedly rebuild custom context management solutions to mitigate this, wasting time and resources.
Solution
Agentified offers a context hydration platform that dynamically assembles only the relevant tools, messages, and memories for each agent session. By registering a toolset once and letting Agentified’s Context Intelligence Layer store entities, relations, and procedural knowledge, the system selects the appropriate subset of tools and knowledge at runtime. This reduces token usage by over 85%, lowers inference costs, and speeds up response times. The platform provides a fluent builder API compatible with TypeScript and Python, delivering a pre‑hydrated, typed context that integrates seamlessly with any agent framework. Continuous learning captures decision patterns and feedback, enabling governance and automatic improvement of tool selection across sessions.
Target Audience
Agentified is aimed at developers and platform teams building AI agents or conversational assistants that need to manage large toolsets and maintain efficient context handling.
Features
- Fluent builder API for registering tools and assembling session‑specific context in TypeScript or Python
- Dynamic discovery that loads only the most relevant tools, cutting token consumption by 85%+
- Context Intelligence Layer that stores entities, relations, user preferences, and procedural skills, then selects needed items per intent
- Automatic learning runtime that records decisions, extracts patterns, and refines tool selection over time
- Governance features that expose decision rationale and prevent repeat errors
- Framework‑agnostic integration, allowing use with any full‑stack or open‑source AI agent architecture