Public acquires Treasury App to expand ai-driven brokerage capabilities. In a significant development announced on May 6, 2026, Public, the AI-powered brokerage platform, has acquired the investing education startup Treasury App. Founded to deliver long-term investment learning through creator-led communities, Treasury App's acquisition marks a pivotal step in enhancing Public's AI-centric investment offerings. The Public brokerage, which has been steadily gaining traction since its launch in 2019, is positioning itself at the forefront of AI-driven retail investing. With backing from major investors such as Accel and Tiger Global, Public has raised over $400 million and currently manages assets in the billions. As part of the acquisition, Treasury App's co-founder Elias Rothblatt will join Public as Senior Director of Operations, with a mandate to drive the development of AI-driven investment products. "With Elias joining our team, this is an important step as we continue building a more intelligent, AI-driven brokerage experience that expands what investors can access and how they invest," said Stephen Sikes, COO of Public. From a user perspective, all existing Treasury App users will have their assets and positions automatically transferred into Public while preserving their current portfolio structure. For those who prefer otherwise, there is the option to transfer assets to another brokerage via ACAT or liquidate their holdings and withdraw funds. Treasury App has been notable for its impact on educational investing. The platform empowered more than 40,000 users to engage in long-term investing and collectively invest more than $100 million. With this acquisition, Public not only gains a sizeable user base, but also enriches its product ecosystem with educational tools and content deeply rooted in the creator economy. Industry observers emphasize the broader significance of this move. As brokerage platforms increasingly turn to AI to differentiate, offering investor education and algorithmic assistance has become essential. Public's acquisition aligns with this strategy, enabling it to integrate educational tools more deeply into its AI brokerage model. The terms of the deal have not been disclosed. However, the strategic fit is clear: Public combines AI-powered investing tools such as Agents, direct indexing, and options rebates with Treasury App's community-driven educational platform. Together, they reinforce the broker's appeal to both novice and experienced investors seeking intelligent, accessible, and engaging investing experiences. With regulatory credentials in place - Public Investing, Inc. is a broker-dealer registered with the SEC, and a member of FINRA and SIPC - the integration of Treasury App is expected to occur smoothly, under proper oversight and compliance mechanisms. In summary, this acquisition signals a powerful evolution for Public's platform: blending AI-enabled investing and educational content under one roof. For investors drawn to tech-forward, learning-oriented brokerage environments, Public is rapidly emerging as a compelling destination.

The rise of agentic brokerage: A dive into the future of autonomous investing. April 2, 2026 The financial services landscape is undergoing structural transformation driven by agentic artificial intelligence (AI) - autonomous systems capable of planning, acting, observing, and adapting without continuous human input. Among the most visible manifestations of this shift is the emergence of agentic brokerage platforms that embed AI agents to research, decide, and execute investment actions on behalf of users. In early April 2026, Public.com began rolling out AI agents that can monitor markets, manage cash, and execute trades - marking what the company calls the world's first agentic brokerage in practice. This article explores how agentic brokerage works, its implications for investors and markets, the broader AI ecosystem trends supporting it, and where the technology is likely headed over the next decade. What is an agentic brokerage? An agentic brokerage transcends traditional online brokerages and robo - advisors by delegating autonomous decision - making and execution to AI agents. Rather than providing tools or recommendations alone, these systems allow users to specify investment goals or tasks in plain language, and the AI agents autonomously: * Monitor relevant market conditions in real time * Manage liquidity and portfolio cash positions * Execute trades when predefined conditions are met * Adjust strategies dynamically based on performance feedback Public.com's agentic features illustrate this model: clients can describe an investing task (e.g., "buy the dip in tech ETFs under specific conditions"), and the platform's AI agents act when the criteria are satisfied. This is a significant shift from robo - advisors, which typically automate portfolio allocation but do not operate with the same level of autonomy or continuous execution logic. Why 2026 is a turning point for Agentic AI in finance. Agentic AI is no longer a theoretical concept; it's entering production at scale across industries. Research and industry data indicate that: * Enterprise adoption of AI agents has surged, with analysts forecasting rapid growth in agentic workflows across sectors such as finance, telecom, and supply chain. * The agentic AI market - including platforms, tools, and services - is projected to expand from approximately $9.1 billion in 2026 to over $139 billion by 2034, reflecting compound annual growth rates exceeding 40%. * Agentic systems are transitioning from isolated tools to operational partners, capable of autonomous actions with real economic impact. In finance specifically, these developments mean that AI agents are poised to move beyond static analytics and into active portfolio management, execution optimization, and personalized investment strategies - tasks traditionally performed by human traders or financial advisors. Core technologies behind agentic brokerage. Several technical pillars enable agentic brokerage: 1. Autonomous planning and execution. Agentic systems incorporate goal - driven reasoning, enabling them to plan multi - step actions and adjust based on real - time outcomes. These capabilities distinguish agentic workflows from simple prompt chains or scripted automations. 2. Persistent memory and context. Unlike single - query models, agentic systems maintain state and context over time, allowing them to remember portfolio goals, risk tolerances, and previous actions - essential for continuous investment strategies. 3. Interoperable agent frameworks. Research into standardized communication and orchestration protocols (e.g., Agent Communication Protocols) is underway to support secure, federated multi-agent systems that can collaborate across platforms. 4. Security and governance layers. As agents gain autonomy, security becomes critical. Threat models extend beyond traditional cybersecurity to include agent hijacking, memory poisoning, and unauthorized action execution - requiring new governance and runtime monitoring frameworks. Opportunities for investors and financial platforms. Agentic brokerage unlocks several potential advantages: Hyper - personalized investing. AI agents can tailor strategies to individual risk profiles and goals, dynamically adjusting to market shifts - something static models cannot do in real time. Efficiency and speed. Agents operate 24/7, executing tasks without human latency, which can be critical in volatile markets. Lower barriers to sophisticated strategies. Retail investors gain access to advanced algorithmic strategies without needing deep technical expertise. Platform differentiation. Financial platforms that successfully implement secure, transparent agentic features could attract users seeking hands - off investing with intelligent automation. Challenges and risks ahead. Despite the promise, agentic brokerage faces significant headwinds: Governance and oversight. Without robust governance, agentic systems can make unexpected decisions with financial consequences. A human - in - the - loop oversight model remains necessary for high - stakes actions. Security vulnerabilities. Autonomous agents interacting with financial systems broaden the attack surface, making agent - aware security frameworks essential. Regulatory uncertainty. Financial regulators will need to define clear frameworks for accountability when AI agents make autonomous trades or portfolio decisions. Market impact. Widespread adoption of autonomous agents could amplify market movements or systemic risks if poorly coordinated. Future predictions: 2026 - 2034. Based on current trends, Lmfx anticipate the following trajectories: 2026 - 2028: maturation and standardization. Agentic systems will move from early rollouts to industry standards, with emphasis on: * Interoperable agent protocols * Transparent decision logging * Integrated governance and compliance tools Financial platforms will compete on trust, explainability, and security. 2029 - 2032: widespread adoption. As agentic systems prove their value, adoption will spread across: * Retail investing * Institutional asset management * Algorithmic trading and risk management By this phase, agents will not only execute tasks but collaborate with human strategists in hybrid decision frameworks. 2033 - 2034: autonomous financial ecosystems. Agentic systems could form interconnected ecosystems where agents negotiate, transact, and optimize portfolios across platforms - potentially reshaping the structure of global financial markets. Conclusion. Agentic brokerage represents a paradigm shift in financial services - blending autonomous AI with real - world investing. The recent rollout of agentic features by Public.com highlights how quickly this frontier is advancing, while market forecasts and enterprise adoption data suggest exponential growth ahead. However, realizing this potential will require robust governance, security, and regulatory frameworks to ensure that autonomous systems operate reliably and transparently. Investors and platforms that navigate these challenges effectively stand to benefit from a future where AI agents act as trusted partners in financial decision - making.