Too Many Agents in the Room? Sounds Like a Spy Movie Gone Wrong! 🙂

The advent of generative AI has opened the floodgates to a new wave of productivity and intelligence tools—AI agents. These agents, designed to automate tasks, enhance decision-making, and streamline workflows, are being embedded into nearly every business platform.

Yet, as the variety of agents grows, a fundamental question emerges: How many agents is too many?

To answer this, we need to examine the three types of AI agent evolutions shaping the landscape. Each type offers distinct advantages and challenges, and understanding their nuances is critical for businesses striving to stay competitive.

1. Agents from Existing SaaS Platforms

These agents are natively integrated into leading SaaS solutions like Salesforce, Workday, or Microsoft Dynamics. For instance, Salesforce’s Einstein Copilot and Workday’s automation tools exemplify how SaaS platforms are embedding AI agents to leverage their proprietary ecosystems.

Strengths

• Deep Integration: SaaS-native agents are deeply embedded into their respective platforms, utilizing the underlying data and workflows to enhance functionality. For example, Einstein Copilot can analyze customer data within Salesforce to suggest the best next steps in a sales cycle.
• Immediate Value: Businesses heavily invested in these platforms benefit from tailored automation and insights without the need for extensive setup.
• Domain-Specific Insights: These agents can provide actionable recommendations specific to their ecosystem, making them invaluable for users already familiar with the platform.

Limitations

• Platform Dependency: These agents are confined to the boundaries of their platforms, limiting their flexibility. For instance, a business using both Salesforce and Microsoft Dynamics might find it challenging to harmonize insights from their respective agents.
• Customer Base Constraints: These agents primarily cater to existing customers, offering little incentive for new businesses to adopt the platform solely for the agent’s capabilities.
• Limited Disruption Potential: While they add depth to the ecosystem, they rarely create new market opportunities or disrupt competitors outside their domain.

Example

Consider a multinational company already entrenched in Salesforce. Einstein Copilot can optimize sales processes, but if the company wants to integrate these insights into a separate procurement tool outside the Salesforce ecosystem, the process becomes cumbersome.

2. Horizontal Platforms to Build Your Own Agents

Platforms like OpenAI’s APIs, AWS Bedrock, and Google’s Vertex AI represent the second evolution in the agent landscape. These horizontal solutions enable businesses to build custom agents from scratch, tailored to specific use cases and independent of any particular SaaS ecosystem.

Strengths

• Customization: Businesses can design agents that cater to unique workflows and processes. For instance, a logistics company could build an agent to optimize delivery routes using OpenAI’s APIs.
• Ecosystem Independence: These agents are not tied to a specific SaaS platform, offering flexibility for companies with diverse tech stacks.
• Innovation Opportunities: Smaller, tech-savvy firms can leverage these platforms to bypass traditional SaaS adoption and create bespoke solutions.

Limitations

• Technical Expertise: Building custom agents requires technical know-how and resources. For example, a startup may struggle to design and deploy an effective AI agent without an experienced data science team.
• Functionality Trade-offs: Unlike SaaS-native agents, which offer seamless out-of-the-box solutions, horizontal agents often require extensive setup and integration.
• Scalability Concerns: The success of these agents hinges on the company’s ability to innovate and continually adapt them to evolving business needs.

Example

A mid-sized e-commerce firm might use OpenAI’s GPT models to create a customer support bot that handles inquiries about order statuses. While this approach provides a tailored solution, the firm must invest in training the model with relevant data, maintaining it over time, and ensuring it aligns with their broader tech stack.

3. Vertical Agents for Specific Tasks

Vertical agents are laser-focused on solving niche problems within specific industries or domains. Companies like Gong, which analyzes sales calls, and Ironclad, which specializes in contract management AI, demonstrate how vertical agents address critical pain points.

Strengths

• Targeted Solutions: These agents excel at addressing specific challenges, such as Gong’s ability to provide actionable insights from sales conversations or an AI recruitment tool that screens candidates based on precise criteria.
• Quick ROI: Businesses can deploy these agents rapidly and see immediate benefits without investing in broader systems.
• Budget-Friendly: For smaller businesses, vertical agents offer an affordable alternative to more comprehensive solutions.

Limitations

• Fragmented Ecosystems: Adopting multiple vertical agents can create silos within an organization, reducing overall efficiency.
• Scalability Issues: These agents often struggle to expand beyond their initial use case.
• Integration Challenges: Lack of interoperability with other systems can lead to inefficiencies, particularly for businesses with complex tech stacks.

Example

A boutique law firm might adopt Ironclad to streamline its contract management process. While this tool drastically improves contract drafting and compliance, it may not integrate seamlessly with the firm’s case management software, requiring manual workarounds.

Also Read – How to Become an Agentic AI Expert in 2025?

Analysis: What Does the Future Hold?

The rise of these three categories of AI agents—SaaS-native, horizontal, and vertical—paints a dynamic picture of the evolving business landscape. While SaaS giants are likely to deepen loyalty among their existing customers, the real disruption lies in the hands of businesses leveraging horizontal platforms and vertical agents.

SaaS-native agents, while impressive, will predominantly strengthen the bond with their existing users rather than significantly expanding market share. Their appeal lies in deep integration and domain-specific functionalities, but they are unlikely to attract businesses outside their ecosystem.

On the other hand, horizontal platforms and vertical agents offer opportunities for businesses to innovate and address niche challenges, often bypassing the need for comprehensive SaaS adoption. These categories empower businesses to experiment with AI agents in ways that align more closely with their specific goals and constraints.

For instance:

• A manufacturing company might use AWS Bedrock to build an agent that predicts equipment maintenance needs based on sensor data.
• A healthcare provider could leverage a vertical agent like Olive, which specializes in automating administrative tasks, to reduce operational overhead while improving patient care.

While these approaches are flexible and impactful, businesses must be cautious about how they incorporate multiple agents into their workflows. An unplanned adoption spree could lead to a chaotic tech environment.

What Does This Mean for Businesses?

The decision to adopt AI agents from one or more of these categories boils down to two critical factors: maturity of the business’s tech stack and specificity of their needs.

For Established Businesses:

Organizations with robust SaaS ecosystems in place—like those heavily invested in Salesforce or Microsoft Dynamics—may find SaaS-native agents like Einstein Copilot or Dynamics 365 Copilot most valuable. These agents add incremental efficiency without requiring new infrastructure investments.

• Example: A retail giant using Salesforce might find Einstein Copilot invaluable for predictive inventory management. By leveraging existing CRM data, it can forecast stock levels based on historical trends and upcoming promotional campaigns.

For Growth-Stage or Fragmented Businesses:

Smaller or growth-stage companies with less cohesive tech stacks often benefit more from horizontal platforms or vertical agents. These tools offer greater flexibility and affordability while catering to specific pain points.

• Example: A regional logistics startup might not be ready to adopt a full-scale SaaS platform like Oracle NetSuite. Instead, it could build a custom route optimization bot using OpenAI’s API, saving costs and creating a tailored solution.

For Highly Niche Needs:

Vertical agents shine when the need is narrowly defined and time-to-value is a priority. These agents provide an immediate ROI by solving specialized challenges without the complexity of broader solutions.

• Example: A financial services firm may choose Riskalyze, a vertical AI agent focused on investment risk assessment, to streamline portfolio management for clients.

Also Read – What Is Agentic AI, and How Will It Change Work?

The Risks of Overcrowding the Room

While the proliferation of AI agents is exciting, businesses must tread carefully to avoid creating a crowded, inefficient tech stack. The risks of adopting too many agents include:

• Redundancy: Overlapping functionalities between agents can lead to unnecessary costs and operational inefficiencies.
• Fragmentation: A fragmented ecosystem can hinder collaboration and data sharing across departments.
• Interoperability Issues: Agents from different sources might not integrate seamlessly, leading to bottlenecks in workflows.

Example of Overcrowding:

A mid-sized marketing agency adopts five different agents: one for CRM automation, another for social media scheduling, a third for SEO analysis, and two more for client reporting and invoice processing. While each agent addresses a specific need, the lack of integration between them results in duplication of effort and data silos.

Key Takeaways

1. SaaS-native agents will deepen loyalty among existing customers but may not attract significant new users.
2. Horizontal platforms empower businesses to innovate and create custom solutions but require technical resources and expertise.
3. Vertical agents address niche pain points effectively but risk creating fragmented systems if adopted excessively.
4. Businesses must prioritize clarity and interoperability over quantity when adopting AI agents. The goal should be to build a cohesive ecosystem where each agent complements the others.

Ultimately, the future of AI agents isn’t about having a crowded room—it’s about having the right agents in the right roles, driving meaningful outcomes for your business.