Context Agents

Part III: Context Agents

Context agents are the mechanism by which the Operating Context System keeps itself alive. They transform a well-designed model from a static snapshot into continuously current operating context.

Today, agents are often used to automate workflows, but they're capable of much more - particularly when they operate within the bounds and goals of a system. If we think of our operating context as a library, then we can think of these agents as the librarians. Beyond helping you find what you need, they're the curators and organizers.

Many platforms build, run, and govern agents. That capability doesn't need to exist within the Operating Context System itself. What matters is that context agents have access to the operating model and can not only maintain it, but improve it.

Context agents operate across three types of work:

Context gathering. Continuously scanning the tools where work actually happens (issue trackers, documents, Slack, code repositories) to find signals relevant to anchor artifacts. When a cluster of engineering tickets looks like it aligns to an initiative, the agent flags the connection for a human in the loop or automatically creates it. When a research document appears relevant to a strategic pillar, the agent surfaces the relationship. The operating context becomes a destination that relevant information flows toward, rather than a system that requires manual updates.

Context maintenance. Monitoring the health of the operating context and taking action to keep it accurate: identifying anchor artifacts that haven't been touched in too long, surfacing missing connections, flagging contradictions between what the context says and what the underlying data shows, and archiving objects that are no longer active. Their primary focus is keeping context fresh, which often includes variables beyond recency.

Context intelligence. Operating at a higher level: finding patterns, surfacing insights, generating readouts, preparing decision support. An agent that notices a dependency between two initiatives that are both at risk. An agent that proactively surfaces that status updates often mention escalations, but escalations are not modeled in the system. An agent that provides early warning signals from deep patterns in the context over time.

The accuracy of context agents is a direct function of the quality of the operating model. Vague definitions attract vague connections. Precise definitions with clear scope, explicit success criteria, and accurate ownership enable agents to make connections with high confidence. This creates a virtuous cycle: humans invest in making definitions good, agents work more accurately, humans spend less time on manual maintenance, they have more capacity to improve definitions further.