Articles

Multi-Agent Systems and Gemini Enterprise: Intelligence as Code

Agentic systems are moving from research to enterprise tools, requiring orchestration to stay reliable, efficient, and secure.

Timothy van der Werf

October 14, 2025

4 minutes

Walk into almost any tech conference today, and you are bound to hear the word agents everywhere. Once a research concept, agentic systems are rapidly evolving into practical enterprise tools. But as these tools mature, a realization is setting in: these systems do not simply need to be built but also require orchestration and operational practices to remain reliable, cost-effective, and secure.

“An agent is basically just a webserver with access to tools and data. It is autonomous, reasons about what to do next, and executes. Once you connect multiple specialized agents, each with their own focus, they can coordinate tasks much like microservices in software architecture.”

This analogy frames a crucial point. Agents are not science fiction; they are architecture. And much like microservices gave rise to DevOps, agentic AI needs its own discipline, AgentOps.

The Rise of Multi-Agent Systems

At their core, agents are LLM (large language models)-powered programs that can call external tools, such as databases, APIs, calculators and another software components. Setting them apart from traditional automation is their ability to decide when and how to use these tools. They are not bound to a script. Instead, they can plan, adapt, and retry in real time.

Scaling this concept leads to multi-agent systems. Instead of relying on one generalist agent, you deploy multiple specialized agents; each one focused on domains like finance, HR, or compliance, coordinated by a central orchestrator. The result is a modular network of intelligences that collaborate like a team of human experts.

Gemini Enterprise brings this enterprise-ready vision by providing:

Pre-built agents for common use cases to accelerate adoption.

Enterprise Search to ground agents in organizational data.

An Agent Development Kit (ADK) for building specialized agents and chaining them into multi-agent systems.

Monitoring and scaling tools, inspired by DevOps observability stacks.

In short, Gemini Enterprise provides the scaffolding to move from prototypes to production-grade agentic systems.

Non-Negotiable: Orchestration and AgentOps

On the surface, multi-agent systems seem to be self-managing: assign a task, and they will divide and solve it. In practice, enterprises face familiar challenges:

Spiraling Costs from agent replication or retry loops.

Variable Latency, one slow agent can bottleneck the entire workflow.

Unpredictable Outputs when agents chain decisions in unexpected ways.

To use a comparison to managing microservices:

“You can set limits on how far they scale, observe their latency, and monitor costs, just like you would with cloud containers.”

This makes orchestration and AgentOps essential, not optional. Organizations need visibility into:

Agent behavior: which tools are used, how often, and why.

System health: latency, errors, retries.

Financial impact: per-query costs, runaway loops.

Governance: access controls per agents, data, processes. Without these controls, agentic systems risk remaining experiments, rather than reliable enterprise assets.

From Monoliths to Modular Intelligence

The parallels with software engineering are remarkable:

Monolithic apps bundled everything into one codebase. Simple at first, but fragile at scale.

Microservices broke functionality into independent, API-driven units, adding complexity but enabling scale and resilience.

Agentic systems face a similar choice:

A monolithic agent tries to do everything, quickly becoming unmanageable.

A multi-agent architecture distributes intelligence across roles, each optimized for a domain, collaborating through orchestration.

The latter is more sustainable. Just as microservices demand DevOps, multi-agent systems are demanding AgentOps. Observability, scaling, and governance become central to success.

The Evolving Role of the Developer

This evolution reshapes the role of development teams.

In an agentic environment, developers shift from writing code to orchestrating ecosystems. Their new responsibilities include:

Defining agent roles within business domains.

Designing coordination mechanisms for task delegation and escalation.

Setting boundaries for data access and tool usage.

Monitoring behavior for cost, latency, and accuracy.

Developers become less like rule writers and more like architects of digital ecosystems. Much like urban planners designing the infrastructure that enables populations of agents to collaborate effectively, they design the infrastructure, governance, and monitoring that enable populations of agents to collaborate effectively.

Enterprise impact: opportunities and challenges

For organizations, there are significant opportunities:

Faster automation through rapid deployment and prototyping Elastic scalability with scalable agents based on demand.

Embedded expertise in compliance, finance or other domains. Accelerated innovation through modular and adjustable roles, prompts and datasets.

However, challenges remain:

Data readiness: agents depend on high-quality, accessible data.

Non-determinism: outcomes are less predictable than in traditional workflows.

Skill gaps: organizations need expertise in data science, software engineering, and operations.

Looking Ahead: Intelligence as Code

We are approaching a future where companies will run hundreds or thousands of specialized, discoverable agents. A procurement agent could consult a compliance agent, which also checks with a legal agent, while under the coordination of a supervisory agent.

What sets this apart from earlier architectures is that these aren’t just services, but reasoning systems that make choices, adapt and learn.

This shift mirrors earlier transformations. Just as infrastructure as code made infrastructure programmable, intelligence as code is doing the same for intelligence. Modular, orchestrated, and observable agents are becoming a new layer of enterprise architecture.

Conclusion

The trajectory of AI has advanced and evolved through multiple waves: expert systems, machine learning, generative AI. In this environment, agentic systems represent the next logical step, adding autonomous reasoning and tool use to generative models, enabling not just answers but action.

The shift from monolithic agents to orchestrated multi-agent systems is the next step in this evolution, echoing the shift from monolithic applications to microservices supported by DevOps. Specialized and orchestrated agents will be supported by AgentOps. Just as DevOps became essential to software delivery, AgentOps will become essential to enterprise AI.

Enterprises that invest early in orchestration, governance, and monitoring will be best positioned to integrate this evolution into their digital core. The result is a new paradigm, the era of intelligence as code has arrived.

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