Lead an agentic SDLC like an organization

Understanding that LLM-based agents are non-deterministic is the key to overcoming this fracture point. This non-determinism implies that we cannot blindly trust AI to get it right, but it also mirrors a fundamental truth about our own workforce: humans are fallible as well. Yet organizations, which are essentially assemblies of fallible humans interacting in complex ways, frequently achieve high performance and consistent results.

How do they do it? Through structures, processes, capabilities and behaviors leadership establishes and management implements and guides.

Managers don't review everything a human is doing, but they are in charge of the system. They design the organization, lead by objectives, intervene when exceptions occur and establish boundaries that allow for productive emergent structures and self-organisation. Leading an agentic SDLC presents similar challenges. If we want to step up and lead agents effectively, we must take on a new steering role in the SDLC and stop trying to review every line of code. I believe that for successful steering we can be inspired by and adopt the practices of the management craft. Orchestrating and steering agents has a lot of parallels with what a manager does when leading an organization.

Example: A developer acts like an Head of AI Workforce. They define the measures of success, e.g. reduce API latency by 20%, and establish a team structure and process, e.g. one agent writes code and a QA agent independently verifies it. The developer intervenes only when a dashboard flags a violation of the objectives or the agents have a dispute.

Cybernetic science as the bridge

For many engineers, viewing management as a "craft" — or even a science — feels alienating, if not entirely suspect. Applying management practices to software engineering may not be obvious. However, Cybernetics can provide a necessary bridge. The concept was introduced in the 1940s by Norbert Wiener and taken up by a diverse group of thinkers ranging from mathematicians to neurobiologists. Cybernetics is a study of communication and control in complex systems, which treats a biological organism, a social organization and a machine as governed by the same universal laws of feedback and regulation.

Stafford Beer developed the viable system model (VSM) in the 1970’s. This connected cybernetics to discipline of management. The VSM is a model of wiring an organization to be viable in a complex world. In the 1980s, Fredmund Malik began integrating the VSM into his work Strategy for Managing Complex Systems, which helped form the backbone of the cybernetic-oriented St. Gallen School of Management. This body of knowledge provides a vast amount of scientific thought leadership that can help engineers treat a system of AI agents not as a black box, but as a functioning, steerable cybernetic system. And, since cybernetics as a science isn’t only applicable to organizations but also to technical systems, it greatly provides the bridge to overcome the friction point of “Human-in-the-loop” in the agentic SDLC.

Example: Consider a self-healing infrastructure with agents. An engineer designs the mechanism where a monitoring agent detects a memory leak and prompts a coding agent to patch it. The engineer doesn't view this as isolated scripts, but as a cybernetic loop where communication between the agents is regulated by feedback, ensuring the infrastructure remains viable and stable under stress.

Leaping to the meta level with steering

In order to understand how a human can effectively lead agents, we must look to one of the foundations of cybernetics: Ross Ashby’s ‘law of requisite variety’: "Only variety can absorb variety." Variety is a measure of complexity, a number of distinct system states. 

In the context of the SDLC, the variety of an agentic system is the overwhelming volume of code changes, design decisions and bug fixes generated at machine speed. In order to control this, the human must have requisite variety (the same or more), which is usually above their cognitive abilities. If a human tries to match this variety at the operative code level by being “in-the-loop” for every change, they will inevitably fail or slow down, reducing variety and, therefore, the speed and capabilities of the agentic system. 

Humans must therefore step out of the problem-solution level and move to a higher level of abstraction: the meta level. Fredmund Malik refers to this as meta-systemic steering. That’s what it means to be “on-the-loop”. This is done by shaping the agentic system, designing and configuring it for achieving desired objectives and adding proper mechanisms to steer the agents towards these objectives. The following diagram shows how the human steps out of the system.

Applying “Go See” with “Human-on-the-Loop”