Agentic AI’s Next Iteration: From Super-AIs to Teams of Specialized Agents — And What It Means for Law & Business

Categories: Antitrust and Unfair Competition, Artificial Intelligence, Contract Disputes, Intellectual Property, Technology

Bryan Hahm

Artificial intelligence is moving beyond standalone large language model wrappers toward collections of specialized AI agents that reason, act, and collaborate to achieve complex outcomes.

This multi-agent vision, articulated in Google’s Introduction to Agents whitepaper,[1] marks a subtle, but seismic, shift in how businesses will deploy AI; and spotlights nuanced legal challenges that litigators and in-house counsel should start addressing now.

At its core, Google’s whitepaper imagines an optimized AI agent environment where an orchestration layer evaluates situations and efficiently deploys specialized agents on a task-by-task basis. Agents will work together in the way that human organizations organically operate—individuals routing tasks, debating actions, and dynamically executing goals across a network of positions. In its most advanced form, multi-agent systems will have the ability to self-evolve and solve problems by creating new AI agents and tools.

Why This Matters

Under this model, businesses will not simply deploy one AI super-agent. Savy businesses will use dozens if not hundreds of agents that each specialize in particular workflows, datasets, and/or tasks (e.g., dataset summarization, contract review, real-time negotiation, client interface, etc.). Importantly, these agents may come from multiple vendors, platforms, and codebases and will require individualized privilege settings and data-security considerations. The legal and operational implications of the incoming distributed agent paradigm are profound.

Benefits of the Multi-Agent AI Systems

Task Specialization: Agents tailored for narrow tasks can significantly outperform monolithic models in accuracy and efficiency, improving workflows in classically siloed areas like procurement, finance, and compliance.
Scale and Flexibility: Companies will deploy agents like contractors, dynamically assembling agent networks that autonomously respond to changing business needs.
Transparency and Compliance: With appropriately designed orchestration layers, systems can audit decisions, trace actions, and enforce company guidelines in real time rather than through recursive, human-intensive audits.

Emerging Legal & Governance Pain Points

As with all new technologies, thoughtful compliance scaffolding is a must. Unlike traditional software, agents act and can autonomously decide on novel courses of action. Cross-agent contracts must contemplate these realities prior to onboarding AI tools, AI agents, and AI-friendly third-party solutions. Business leaders and counsel should consider the following:

What actions may agents take on behalf of users and organizations.
What data may agents share, retain, or expose to users or to third-party agents.
Which decisions should require mandatory human-in-the-loop (HITL) review.
How should responsibility, auditability, and liability be allocated across agents and humans.

Without clear governance protocols, businesses risk inadvertent breaches of privacy, terms of service violations, and internal confusion.

Canary in the Coal Mine: Amazon v. Perplexity

In the fall of 2025, Perplexity’s AI browser agent, Comet, began autonomously shopping on Amazon’s platform on behalf of Perplexity’s users. Amazon, displeased, alleged that Comet’s agentic actions violated its Terms of Service and posed security risks by disguising automated activity as human browsing. Amazon promptly escalated the disagreement from a cease-and-desist to federal litigation. Perplexity decried Amazon’s position and motion practice as an attack on innovation and user choice; stating, “Bullying is Not Innovation.”[2]^{, [3]}

This dispute highlights developing legal questions in an agent-facilitated world:

Must agents identify themselves as automated actors?
What legal standards define the actions of automated actors?
Who will define the guardrails for autonomous agent actions over the worldwide web?

Amazon’s complaint invokes traditional doctrines of contract law and computer fraud.[4] However, the true takeaway of this dispute is that a wild west of negotiation and litigation is dawning. Only attorneys with a genuine understanding of the space will be equipped to play ball.

Practical Early Solutions for Businesses

To prepare for this new era of agentic AI, business leaders should plan to delegate the following tasks:

Build Agent Governance Frameworks: Define roles, access rights, decision thresholds, logging requirements, and HITL triggers for each class of agent utilized.
Draft Clear Contracts & Software Licensing Agreements: Require vendors to explicitly define agent behavior, compliance regimes, and agent decisioning logic.
Implement Audit & Trace Mechanisms: Ensure every agent’s actions are recorded and attributable.
Monitor Third-Party Agent Interactions: Establish policies for external platform engagement, including service provider terms and conditions.

The shift to multi-agent AI systems promises tremendous efficiencies for legacy organizations in industries like healthcare, pharma, finance, and government. For newer organizations, multi-agent systems promise compounding efficiencies from day-one. As such, counsel and business leaders must rethink governance, contracts, and compliance strategies to ensure AI agents act lawfully, transparently, and in alignment with business risk tolerances. Those that do will be the success stories of the next AI frontier.