Inflectiv 2.1 marks the most significant platform update since launch. At its core is a fundamental shift in how agents interact with data, from passive consumers to active learners. Alongside this, the release introduces ElizaOS agent integration, expanded file format support, and a suite of platform improvements that move Inflectiv closer to production-grade infrastructure that teams and builders can depend on daily.

This article covers every major feature in the release, what it enables, and why it matters for the intelligence economy.

Self-Learning Intelligence API

Agents on Inflectiv can now write knowledge back into datasets, building structured intelligence autonomously over time.

Until this release, the Intelligence API was read-only. Agents could query datasets, retrieve structured answers, and operate on fixed knowledge. That model works well for production workflows where consistency and determinism are essential.

But real-world intelligence is not static. Research accumulates. Markets shift. Regulations update. An agent monitoring cryptocurrency sentiment today needs to capture what it learns and make that knowledge available for future queries, without a human manually updating the dataset.

Release 2.1 introduces a bi-directional Intelligence API. External agents can now read from and write to datasets, creating a continuous knowledge accumulation loop.

Two Modes, One Infrastructure

Read-Only Mode: The dataset is locked. Agents operate on fixed, trusted data. No modifications allowed. This mode is built for production environments, compliance workflows, and any scenario where deterministic outputs matter.

Self-Learning Mode: Agents can read and write. An agent browsing the web, scanning documents, or monitoring live data feeds can continuously grow its own structured dataset inside Inflectiv. Every entry is automatically tagged with provenance, so you always know what came from where and which agent wrote it.

You can switch between modes at any time through the API. A dataset might start in Self-Learning mode during a research phase, then lock to Read-Only once the knowledge base reaches maturity.

Built-In Safeguards

Self-learning agents without guardrails can create runaway datasets with duplicate or low-quality entries. Inflectiv addresses this at the infrastructure level:

• SHA-256 deduplication: Every incoming entry is hashed. Duplicates are detected and skipped automatically at zero credit cost.

• 10,000-entry dataset cap prevents uncontrolled growth. Keeps datasets focused and queryable.

• Full provenance tracking , Every entry records which agent wrote it, when, and from what source.

• 1 credit per new entry. Duplicates are free. You only pay for genuinely new knowledge.

• Batch writes up to 50 entries. Efficient bulk ingestion for agents processing large volumes.

What This Enables

The Self-Learning API transforms what is possible on the platform:

• A market intelligence agent that logs structured signals from crypto markets daily, building a proprietary dataset that grows more valuable over time.

• A research agent scanning academic papers that accumulates findings into a queryable knowledge base , weeks and months of research, structured automatically.

• A compliance bot that monitors regulatory updates and builds its own database of rules, changes, and requirements.

• Any agent that interacts with the world can now capture what it learns and make that knowledge reusable, queryable, and permanent.

ElizaOS Agent Integration

Two AI Backends. One Platform
Create agents powered by either the Inflectiv Agent (OpenAI/Grok) or ElizaOS, an open-source AI framework with rich personality systems.

ElizaOS is an open-source agent framework built around deep character configuration. It allows developers to define agent personality through bio, topics, adjectives, conversational style, lore, and message examples, creating agents that feel distinct and intentional rather than generic.

With this integration, Inflectiv now supports both backends within the same infrastructure. Developers choose their backend when creating a chatbot and can switch between them at any time.

What ElizaOS Brings

• Rich character configuration , bio, topics, adjectives, conversational style, and lore

• Native personality modeling with message examples

• Full RAG support , knowledge retrieval works identically across both backends

Both agent types share the same dataset infrastructure, credit system, and API access. External integrations work the same regardless of which backend powers the agent. This means developers can experiment with ElizaOS personalities without rebuilding their data pipeline or changing how they query agents through the API.

Parquet and XML File Support

Inflectiv now accepts Apache Parquet (.parquet) and XML (.xml) files as knowledge sources, joining the existing support for PDF, DOCX, CSV, JSON, and other formats.

Parquet

• Powered by pandas

• Automatic column flattening for nested structures (up to 3 levels deep)

• Dot-notation paths preserved for traceability

• 100,000-row safety limit to prevent memory issues

XML

• Powered by xml

• Recursive parsing with namespace handling

• 3-level depth traversal with path preservation

• Automatic sanitization and chunking

Both formats integrate seamlessly into the existing knowledge pipeline. Upload through the UI or API, and data becomes searchable within minutes. For teams working with analytics exports (Parquet) or legacy enterprise systems (XML), this removes a manual conversion step that previously blocked ingestion.

Email and In-App Notifications

Inflectiv 2.1 introduces two notification systems designed to keep users informed without leaving the platform or missing critical events.

Email Notifications

Transactional email notifications now cover key account events:

• Welcome email on signup

• Knowledge processing , success, and failure notifications when datasets finish processing

• Credit alerts , warnings when the  balance drops below 50 credits, and when it hits zero

• Purchase confirmations , receipts for subscriptions, and credit top-ups

• Payment failure alerts and subscription change confirmations

All emails respect user notification preferences. Manage them from account settings under email_billing and email_knowledge toggles.

Real-Time In-App Notifications

A notification bell in the header delivers real-time updates via Server-Sent Events, no page refresh needed. Notifications cover bot creation, knowledge processing status, credit balance warnings, marketplace activity (datasets and agents acquired, sold, or reviewed), and agent invitations.

Features include unread badge count, a dropdown panel with mark-as-read functionality, clickable notifications with direct action URLs, and automatic 90-day cleanup. Available on both the main Inflectiv platform and the DogeOS frontend.

Intercom Integration

Live support is now embedded directly inside the platform. Intercom powers a conversational support widget on every page with AI-powered initial responses via Intercom Fin and seamless handoff to human support when needed.

Security is handled through HMAC-SHA256 identity verification. The support team sees full user context, subscription tier, credit balance, and account status, so conversations start with complete visibility rather than troubleshooting from scratch.

What This Release Means

Inflectiv 2.1 is not a collection of incremental improvements. It represents a structural shift in what the platform enables.

The Self-Learning Intelligence API moves agents from passive consumers of static data to active participants in knowledge creation. ElizaOS integration opens the platform to an entirely new builder community with a different approach to agent design. Expanded file support and production-grade notifications bring the platform closer to the kind of infrastructure that teams depend on without thinking about it.

Every feature in this release serves the same thesis: intelligence is not static, and the platforms that treat it as a living, evolving resource will define the next era of AI infrastructure.