Artificial intelligence is moving beyond standalone large-scale language model wrappers to collections of specialized AI agents that reason, act, and collaborate to achieve complex outcomes.
This multi-agent vision is based on Google’s Agent introduction white paper,[1] This marks a subtle but dramatic shift in the way companies deploy AI. And we highlight sensitive legal challenges that litigators and in-house lawyers should start tackling now.
At its core, Google’s whitepaper envisions an optimized AI agent environment where an orchestration layer assesses the situation and efficiently deploys specialized agents for each task. Agents work together to allow individuals to route tasks, discuss actions, and execute goals dynamically across a network of roles, just as a human organization operates organically. In its most advanced form, multi-agent systems have the ability to self-evolve and solve problems by creating new AI agents and tools.
why is this important
In this model, companies don’t just deploy one AI super agent. Savy companies use dozens, if not hundreds, of agents, each specialized for a particular workflow, dataset, and/or task (for exampledataset summarization, contract review, real-time negotiation, client interface, etc.). Importantly, these agents can come from multiple vendors, platforms, and codebases, requiring separate permission settings and data security considerations. The legal and operational implications of the upcoming distributed agent paradigm are profound.
Benefits of multi-agent AI systems
- Task specialization: Agents tailored to narrow tasks significantly outperform monolithic models in terms of accuracy and efficiency, improving workflows in traditionally siled areas such as procurement, finance, and compliance.
- Scale and flexibility: Enterprises deploy agents like contractors, dynamically building agent networks that autonomously respond to changing business needs.
- Transparency and Compliance: A well-designed orchestration layer allows systems to audit decisions, track actions, and enforce corporate guidelines in real-time, rather than recursive human audits.
Emerging legal and governance issues
As with any new technology, it’s essential to have a compliant footing. Unlike traditional software, agents can act and autonomously decide on new courses of action. Agent-to-agent agreements must consider these realities before deploying AI tools, AI agents, and AI-enabled third-party solutions. Business leaders and lawyers should consider:
- Actions that an agent can perform on behalf of a user or organization.
- Data that agents can share, retain, or publish to users or third-party agents.
- Which decisions require mandatory human intervention (HITL) review?
- How should responsibility, auditability, and liability be allocated across agents and humans?
Without clear governance protocols, companies risk inadvertent privacy violations, terms of service violations, and internal disruption.
Canary in the coal mine: Amazon vs. Perplexity
In the fall of 2025, Perplexity’s AI browser agent Comet began autonomously shopping on Amazon’s platform on behalf of Perplexity users. Amazon was unhappy, claiming that Comet’s agent actions violated its terms of service and posed a security risk by making automated activity look like human browsing. Amazon immediately escalated the disagreement from a cease-fire agreement to a federal lawsuit. Perplexity condemned Amazon’s positions and actions as an attack on innovation and user choice. “Bullying is not innovation,” he said.[2], [3]
This dispute highlights evolving legal issues in an agent-driven world.
- Should agents identify themselves as automated actors?
- What legal standards define the behavior of automated actors?
- Who defines the guardrails for autonomous agent actions on the World Wide Web?
Amazon’s complaint draws on traditional contract law and computer fraud doctrine.[4] But the real point of this dispute is that a wilderness of negotiations and litigation is beginning. Only a lawyer who truly understands this field can actually act.
Practical early solutions for your business
To prepare for this new era of agentic AI, business leaders should have a plan to delegate the following tasks:
- Build an agent governance framework: Define roles, access rights, decision thresholds, logging requirements, and HITL triggers for each class of agents you want to use.
- Draft clear contracts and software license agreements: Require vendors to explicitly define agent behavior, compliance regime, and agent decision logic.
- Implement auditing and tracking mechanisms: Ensure that all agent actions are recorded and accountable.
- Monitor third-party agent interactions: Establish policies for working with external platforms, including service provider terms of service.
The transition to multi-agent AI systems promises to bring significant efficiencies to traditional organizations in industries such as healthcare, pharmaceuticals, finance, and government. For new organizations, multi-agent systems promise increased efficiency from day one. Lawyers and business leaders must therefore rethink their governance, contract, and compliance strategies to ensure that AI agents act lawfully, transparently, and in line with business risk tolerance. Those who achieve this will be the next success story on the AI frontier.
Endnotes
[1] https://www.kaggle.com/whitepaper-introduction-to-agents (last visited January 21, 2026).
[2] https://www.perplexity.ai/hub/blog/bullying-is-not-innovation (last accessed January 16, 2026).
[3] https://terms.law/2025/11/03/amazon-vs-perplexity-when-a-cease-and-desist-letter-calls-your-ai-a-computer-fraud/?utm_source=chatgpt.com (last accessed January 16, 2026).
[4] https://terms.law/2025/11/03/amazon-vs-perplexity-when-a-cease-and-desist-letter-calls-your-ai-a-computer-fraud/?utm_source=chatgpt.com (last accessed January 16, 2026).
