Brightwave’s Background Agents Enable Singular Control of Agent Fleets

Brightwave, an agentic AI research platform, has introduced background agents, enabling users to control fleets of research autonomous agents from a single, unified control plane. This release enables multi-agent research systems.

Inspired by system and user interface design patterns drawn from AI-powered software engineering tools like Cursor, Brightwave’s agentic technology allows the system to spawn long-running workers that can perform complex, open-ended tasks autonomously without interrupting users’ research flows.

This technical approach enables Brightwave to infer a research plan from a user’s request, defining at inference time the sequence of activities required to accomplish a research goal, such as creating a long-form deep research report, cloning a previously-authored document, refining and updating a document, or constructing an information-dense chart or evidence-linked table.

Given a research plan, an asynchronous background agent uses a host of search, reasoning, fact-checking, planning and presentation tools to accomplish that goal. Once the background agent believes it has accomplished its task, it submits its work product to an LLM judge, an independent system that uses powerful reasoning models to assess whether the output satisfies the original user requirements.

Much like a software engineer would use unit tests to determine whether a piece of code satisfies a set of requirements, Brightwave uses these satisfaction criteria to ensure users receive high-quality, grounded research deliverables from every Brightwave interaction.

“The future of AI systems is all about deliverables. AI’s impact on software engineering is a great example. Lightweight, asynchronous feedback on complex work products wins against close management every time,” said Mike Conover, CEO and co-founder of Brightwave.

With background agents, users can define a high-level research objective with succinct natural language instructions and the system reasons independently about what data sources should be considered. For example, Brightwave can prompt the platform for reports on the real estate and energy players likely to be positively impacted by a recent executive order on AI, and using data from sources like whitehouse.gov, public filings and regulatory disclosures, Brightwave will produce a visualization-rich deep research report asynchronously. From there, users can continue to ask clarifying questions, edit and refine the substance of the report itself, or deepen their understanding of the primary sources Brightwave has discovered.

“We’ve been focused on realizing this vision since we started the company,” continued Conover. “From day one, it was clear to our team that this type of autonomous research is where the market would go, and every engineering and product decision we’ve made is a reflection of that belief. That’s the reason Brightwave works better than any other product on the market – it was designed for this moment.”

Agentic is generally available, and new users can trial the platform directly for free—no enterprise contract required.



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Reg A - Form 1-A Filings Beta

Beta. Initial Form 1-A only. Target raise is the stated Amount to be Raised, totaled. Compared with the same dates one year earlier.

YTD: Jan 1, 2026 - Oct 22, 2026 vs Jan 1, 2025 - Oct 22, 2025

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Number of Filings130Prior 125 +4.0%
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This YTDYear priorChange
Filings 130 125 +4.0%
Target raise $3.37B $3.54B -4.7%

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