@RobGibson20 In regulated industries, AI adoption used to be mostly read-only. Models drafted emails, summarised policies, and helped people search internal knowledge.
The development of agents provides a significant shift. An agent isn’t just a better interface to a model; it’s a system that can take actions, such as opening pull requests, rerunning CI, filing tickets, and coordinating work across tools. The moment an AI system can operate within your environment, the conversation shifts from one about cleverness to one about governance: identity, permissions, audit trails, change control, incident response, and crucially, where execution runs.
In 2026, the real winners will be the teams that treat agents like production software. Developer-first agents are systems with clear rules, rigorous testing, full visibility, and minimal access. For many CTOs in regulated industries, that architecture naturally points toward on-prem/air-gapped deployment as a governance requirement.
Why 2026 will shift AI adoption
There are two big reasons why we’re seeing this shift right now.
The first reason is that adoption pressure is mounting. Gartner has been clear, forecasting that task-specific AI agents will be a standard feature in 40% of enterprise applications by the end of 2026. When a shift this big happens, the debate widens from whether agents should be used to how they can be operated safely, and at a massive scale.
The second reason is that governance is tightening, both from security bodies and regulators. OWASP’s guidance on LLM applications highlights risks such as prompt injection and excessive agency precisely because systems that can act create a larger blast radius than systems that only speak. Similarly, NIST’s AI Risk Management Framework and the EU’s AI Act are pushing the industry away from a "figure it out later" mentality, demanding that risk management be treated as a continuous cycle: design, deploy, evaluate, monitor, and improve.
Put those together, and you see the bigger picture of AI in 2026. Agents are becoming mainstream, and the expectations of governance are higher than ever. The opportunity is huge, but the bar is higher than most agent demos suggest.
For instance, when people say “agents are unreliable,” they’re often pointing at model behaviour, such as hallucinations or inconsistencies. However, most production failures described in real deployments don’t resemble a model making something up. They look like classic software and automation failures such as: an over-permissioned workflow modifies the wrong thing; a tool call executes a side effect without a gate; an incident happens and nobody can reconstruct the sequence of actions because logs are incomplete; an agent gets stuck in a retry loop and quietly burns budget; a tool contract changes and breaks an integration in ways that are hard to detect.
If your agent is allowed to act, then reliability is all about governance. The model is just one piece of a much larger system that includes policies, tools, security boundaries, and operational controls. You can’t simply prompt your way out of missing engineering fundamentals. You have to build agents exactly the way you build every other critical production system.
What “developer-first agents” actually mean
A developer-first agent is built on the simple assumption that it must live by the same rules as all your other critical production services. It must be reviewed, tested, monitored, and held fully accountable.
Tool Contracts – Tools are robust APIs. That means they need schemas, versioning, input validation, clearly defined failure behaviours, and, crucially, tests. This is why tool-first architectures are becoming the standard, where reliable agents need explicit tool interfaces, not just free-form text instructions. An untyped, untested tool is a security incident waiting to happen.
Least Privilege and Explicit Agency – Take the OWASP warning about excessive agency to heart. The truly dangerous failure is an agent that can execute high-impact actions with unlimited credentials. Developer-first systems always default to tight scopes, requiring explicit approvals for sensitive actions, and clearly separating the "decide" role (the model) from the "do" role (a human orchestator).
Observability and Auditability – Modern agent stacks treat tracing and run logs as essential features. Without them, you can't satisfy compliance or troubleshoot operations; it's all guesswork. A developer-first agent must easily answer: what tool was called, with what input, by whom, under what policy, what was the exact output, and what happened next?
Continuous Evaluation – Agent behaviour is always changing, even if you don’t change your code. Models are updated, data sources shift, or your prompts get more complex. Evaluation can't be a single checklist item at launch; it has to be part of ongoing operations. This means having regression suites for your most important workflows, scenario testing, and safely canarying any changes to prompts, policies, tools, or model versions.
Why deployment matters for governance
There's a massive difference between using a SaaS chat assistant and running an agent that can touch your internal code, tickets, and CI/CD pipeline. The second category handles data, uses credentials, and can execute permanent changes.
That's why sovereignty and data-boundary controls are now mainstream priorities. For high-impact workloads, many enterprises want the agent's execution to happen inside a security boundary they control, whether that’s a sovereign cloud or, frequently, an on-premise boundary.
A good principle for enterprise teams is this: If an agent is taking an action that would be unacceptable without an audit trail and scoped permissions, then you must assume its execution needs to be governed in the same way you manage every other piece of production automation. For many regulated teams, on-prem or air-gapped deployment is the cleanest, most pragmatic way to meet this requirement, especially when dealing with sensitive, proprietary code.
Where Cosine fits in
If you agree with the developer-first philosophy, the evaluation criteria for a platform become very simple. You need to know:
Where does the code run?
How is identity handled?
How are secrets managed?
What does the audit trail look like?
Does the agent integrate into your existing SDLC or try to bypass it?
Cosine is built specifically around these criteria, with on-premise air-gapped Kubernetes as the primary target for regulated industries. While Cosine has a hosted offering, our core enterprise focus is the air-gapped Kubernetes model, currently in use by major financial and governmental institutions. For Azure-standardised customers, it can be deployed on Azure Kubernetes, using Azure OpenAI for inference.
Most importantly, Cosine is engineered to participate in the SDLC, not circumvent it. Users submit tasks from the places they already work – GitHub, Bitbucket, Jira, Linear, and Slack – and Cosine works asynchronously to implement the change, test it, and submit a pull request. For planning without risk, Research Mode lets the agent propose and refine an approach before writing code. Users can also operate within the Cosine CLI, accessing local tools. And when you need speed, Cosine can orchestrate multiple agents working in parallel across a repository, each mapped to its own PR, maintaining perfect isolation and a clear audit boundary.
Building agents like production software
Agents are becoming inevitable. The path to value is not to chase autonomy for its own sake, but to build systems that can be trusted: tools with contracts, actions with scoped permissions, behaviour with evaluation, and operations with observability.
In regulated industries, the further you move from assisting to acting, the more your deployment model becomes a core part of your governance model.
Developer-first agents are the essential bridge that takes a project from a flashy demo to secure, governable automation. Get the engineering fundamentals right, and agents will dramatically increase your capacity without compounding your risk.
