AI agents aren’t just software. They’re autonomous actors, goal-driven and equipped with probabilistic logic. To scale them into the enterprise world, organizations must combine software development rigor with the principles of employee management, says Gaurav Suman, Director of AI and Industry Solutions, Solace. But doing so effectively requires them to live in a real-time, event-driven world so agents can communicate asynchronously and access live contextual data to learn, function and improve.
The software industry has long understood that reliable systems require a structured, repeatable way of building them. Without a disciplined approach to how software is planned, built, tested and maintained, quality erodes and systems become impossible to scale, yielding software that’s fast at first but brittle forever. This truth gave birth to the software development lifecycle (SDLC), a multi-step framework that codified methodology as a core ingredient of reliable software delivery.
AI agents are also software, but not software “as we know it.” Built on probabilistic language models, large or small (LLM/SLM), there are fundamental differences in how agents are built, evaluated and governed. Introducing them into an organization needs an agent development lifecycle (ADLC) applying SDLC’s philosophy of structured, repeatable process to the development and management of AI agents.
With help from HR practices
In Singapore, Singtel is redesigning its organizational structure where managers oversee mixed fleets of humans and AI agents. Meanwhile, GovTech is developing a registry to track the owners and activities of AI agents used by 150,000 public officers. These developments point to a simple idea: since AI agents perform roles within an enterprise, the ADLC should also draw from the human employee lifecycle.
When an organization hires a new team member, there’s a deliberate process: their role is defined; access is granted to systems they need; they’re trained and integrated into a team. Once empowered, a manager oversees and monitors their performance over time.
As autonomous actors operating within a system of governance, AI agents deserve and require that same treatment; they need real-time contextual data to do their jobs.
Don’t skip the AI “hiring process”
Picture an organization where every employee is a generalist, with no hierarchy or structure, communicating only via synchronous one-to-one phone calls, with no emails, Slack or shared systems. That organization can’t scale. Information doesn’t flow. People drown in data, with no real understanding of what to do. There are no accountability structures, access controls or coordination mechanisms beyond individual conversations.
You wouldn’t drop a new human hire into this chaos. Effective organizations instead employ specialized people, organized hierarchically, who coordinate asynchronously. Onboarding AI agents requires that same approach.
Agent mesh treats agent hierarchy the same way as human employees
Most early agentic AI deployments work as complex monolithic agents tasked with doing everything, accessing all data or maintaining large context with sub-agents. The result? A system that’s brittle, expensive, inconsistent and unable to handle real enterprise complexity.
This is where an agent mesh acts as a development and runtime platform, helping build AI agents for a real-time enterprise, specialized for particular functions, organized hierarchically and orchestrated by a development and runtime platform that delegates tasks to appropriate agents. Communications between agents are asynchronous and event-driven, while role-based access controls give agents exactly the permissions they need and nothing more.
So, let’s look at how agents can be “onboarded” through an ADLC much like a new employee joining the organization:
1. Hiring: Define role, responsibilities, expectations and guardrails
Before a human employee starts, you write the job description, defining their role, responsibilities, expected behaviors and boundaries.
An agent mesh’s builder uses an internal AI agent to guide the setup of an agent’s purpose, scope and configuration. You set the instructions and system prompts shaping the agent’s persona and configure guardrails, hard constraints that prevent it from going off-script or taking unsafe actions.
2. Onboarding: Give access to systems and tools
A new employee’s first weeks are spent getting access to tools, systems and data. Agent onboarding is the same.
An agent mesh should include pre-built integrations to enterprise databases, data warehouses, data lakes, APIs and MCP servers. Role-based access controls (RBAC) enforce least-privilege principles so agents only see what they need, while defined skills help agents use their tools well.
3. Coaching: Internal training to ensure competence
After getting access to systems, human employees undergo training to turn general ability into job-specific skills. The same logic applies here.
An agent mesh can provide an Eval function that uses AI to suggest tests for agents, lets you add more and runs them against your agents. This empowers the organization to validate agent competence against defined success criteria, supporting both initial and regression testing as you make changes.
4. Supervision: Trust, but verify
Even capable employees get close oversight when new to a role. Supervision isn’t micromanagement; it’s the safety net that ensures quality and catches errors before they compound.
An agent mesh with human-in-the-loop architecture can route specific agent actions or decisions to human reviewers before execution. This matters as LLMs are non-deterministic and make mistakes, so when the impact of agents being wrong is too risky, humans can validate their actions, responses or conclusions.
5. Teamwork: Where the real value emerges
The most transformative phase of the employee lifecycle is when individuals become part of high-performing teams, where collective capability exceeds the sum of its parts. An agent mesh can support repeatable workflows, such as steps in approving a loan or providing an insurance quote. Dynamic orchestration is where the mesh comes alive: orchestrator agents route work to the right specialists, in sequence or in parallel, adapting in real time when the path forward requires reasoning.
The result is a multi-agent mesh topology, hierarchical agent organizations coordinating specialist agents across various functions, mirroring the structure of an effective human organization.
6. Improvement: Deployment is not the finish line
Effective organizations don’t deploy employees and forget about them. They monitor performance and provide feedback for continuous improvement.
An agent mesh’s visualizer gives a real-time graphical interface for tracing agent interactions, tool and LLM calls, and decision pathways. Ongoing evaluations detect performance drift or emerging failure modes via online evals in the background, monitoring production execution. Further, OpenTelemetry instrumentation surfaces performance trends, giving you data to make informed decisions about when and how to tune your agents.
Scaling AI requires managing agents like employees
Moving AI agents from experimental prototypes to mission-critical enterprise assets requires a fundamental shift in how we think about agent delivery. SDLC provides discipline for deterministic code, but autonomous, probabilistic agents require more: clear roles, secure system access, rigorous testing, human supervision and continuous performance management.
By combining the structural discipline of the ADLC with a real-time and event-driven platform, enterprises can confidently deploy, orchestrate and govern multi-agent systems at scale.
Gaurav Suman is Director of AI and Industry Solutions at Solace Corporation. He works at the intersection of enterprise technology and the practitioners who buy, sell and implement it, with a focus on real-time data and AI use cases.
Editor’s note: This contributed article has been lightly edited for TNGlobal style and clarity. The views and arguments expressed remain those of the author.
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