It's easy to forget that we're at the very beginnings of the AI era. In the last eight months alone software engineering has witnessed leaps that no one in the industry previously thought possible, turning months into what feel like years.
That speed has created a recency bias in how we believe agent work should operate, mainly in the assumption that it should start with a prompt. But the further the frontier moves, the clearer it is that prompts only scratch the surface of what autonomous agentic workforces can actually achieve.
Prompting keeps a person in every decision from start to finish, so agents can only take on as much work as that person has time to describe and manage. Prompting also assumes a person already knows what they need, often long before they actually do.
This pattern mimics how search has operated historically: a person types a request and gets something back. That works for now. But agents that act on their own are opening up ways of working that nobody has fully imagined yet.
We're only beginning to see what more autonomous agents can look like and the many layers needed to get us there.
Multiplayer agentic workforce orchestration
At the foundation is orchestration, where a central leader directs the work of an agent team beneath it, such as Lead Dev managing its team of named specialist agents with individual track records on Codespeed. Good orchestration uses loops and checks to keep agents' work at the team's standard and on track toward its goals, so agents don't run off course or in circles, which even the best frontier models tend to do.
Multiplayer is a key component of agentic workforce orchestration. It lets a whole team of people shape what their agents work toward together, and in the most innovative systems, including Codespeed, it builds recursive self-improvement that delivers more valuable output as agents learn over time.
Once an agent team can run on its own, agents can take on new kinds of work, and even start valuable work themselves, because they understand the business and its goals.
Agents need context and access
Autonomous agents also need context and access. An agent does its best work on its own when it can reach the systems where the company's knowledge lives.
This is why connectors to data sources and other tools have become an essential area of innovation for platforms like Codespeed that deliver fully formed agents.
A shared company brain between agents and people
But access to a customer's billing system, for example, is only useful if the agent knows why and how it should change anything there. Skills written for one person's agent have been useful, but now whole teams work with agents, so the company's shared knowledge and ways of working matter more than ever.
Codespeed's Open Knowledge Protocol, released in March 2026, provides the foundation for agentic operating systems in any business, including essential business context inside the codebase itself. From this foundation, any type of agent can deliver value, because business knowledge lives in one central location and becomes a shared company brain for both agents and people. Google released its own Open Knowledge Format in June 2026, and the Codespeed platform supports it too.
For example, a Codespeed specialist agent asked to continuously improve the customer experience might notice that many new customers are quitting at the same step, which may prompt it to read the support conversations about the drop-off. That could lead to a quick bug fix or, better yet, higher conversion and growth for the business far beyond the fix itself.
Autonomous agent-led growth
A few weeks after the customer experience specialist makes its conversion-boosting fix, it notices in those same support conversations that many customers are asking to connect the product to the accounting software they already use. It brings the team a proposal to build that integration, with the customer requests attached, because it understands what the business is trying to do.
Governance and security controls are a crucial part of the autonomous agent picture. Autonomy can take many shapes and grades. On Codespeed, teams decide what the agent is working toward, which systems it can reach, how much it can spend, and which changes need a person's sign-off.
Named specialist identities also ensure an agent has a track record, trained by the team. Over months, the agent's choices start to look like the choices the team would have made, and the team spends its time on the decisions that matter most.
