What makes autonomy work?

Imagine an AI agent reviewing your pull request – not just flagging issues, but proposing meaningful improvements, refactoring code to be more readable, more efficient, more secure, and even identifying follow up tasks before you do. That’s the future of agentic AI in software development. But how do we get there?

The answer lies in a principle we already follow when working with people: trust must be earned before autonomy is given. You probably wouldn’t ask a new team member to make an important project decision on their first day. But as they prove their skills and judgment, you grant them more freedom – merging pull requests independently, suggesting architectural improvements, even identifying areas of technical debt ahead of becoming bottlenecks. AI should follow the same path. So we must ask: What makes autonomy work? And what conditions must be in place for developers to trust AI agents the same way they end up trusting their most capable teammates?

How AI earns autonomy in a development team

For developers to grant AI autonomy, two key dimensions of trust must be established: competence – the ability to execute tasks successfully – and alignment – the ability to understand the team’s goals and work in sync.

Competence: The ability to take action successfully

Developers won’t trust AI to act independently unless it demonstrates that it can:

• Generate high-quality code that meets the team’s standards
• Handle varied and complex development scenarios without excessive human correction
• Learn from developer feedback and improve over time

A key aspect of this is latent learning – AI must absorb information from past interactions and apply it effectively when the situation calls for it. Imagine an AI-powered coding assistant that doesn’t just suggest syntax fixes but gradually learns an engineering team’s preferences, adapting to their coding style and proactively refactoring inefficient patterns.

Competence doesn’t exist without reliability and repeatability. Neither a human nor an AI developer that occasionally produces great suggestions but is inconsistent will earn autonomy. Developers need AI to:

• Offer stable, predictable improvements rather than sporadic breakthroughs
• Explain its reasoning clearly (e.g., why it suggests a certain code optimization)
• Avoid making careless changes that introduce regressions

A useful real-world parallel is automated testing – a tool that occasionally flutters between false positives and missed bugs is not trusted, whereas one that consistently catches regressions earns a place in the CI/CD pipeline. AI must build a similar track record of dependability before teams allow it to act without review.

Alignment: The ability to understand team goals and act accordingly

Proving competence is only part of the equation. Even the most capable AI won’t be trusted if it works at odds with a team’s goals. That’s where alignment comes in – ensuring AI acts in ways that support the team’s bigger picture. Developers don’t just want AI to generate code; they need it to make decisions that support the project’s broader goals, including:

• Adhering to team conventions and architectural principles
• Identifying areas for improvement that align with current priorities
• Helping maintain technical debt rather than blindly optimizing for speed

The key is aligned autonomy. An AI agent that starts working without accounting for the most up-to-date team priorities can create more work than it saves. For example, if an AI agent detects a slow-running function and starts aggressively optimizing it, but the team is focused on bug fixes before a release, its actions could create unnecessary distractions. Without clear alignment, AI risks solving the wrong problems – or even spinning up new ones.

This means AI must be given clear goals, and work within explicit guardrails and feedback loops – much like a new engineer learning a team’s expectations over time. AI should provide suggestions, receive feedback on those suggestions, and refine its decision-making process accordingly. The goal isn’t just accuracy. It’s meaningful, context-aware contributions.

The gradual path to AI autonomy in development

Just as junior developers or new teammates gain responsibility incrementally, AI should be introduced into the development workflow step by step. This ensures that trust is built through progressive autonomy, where AI takes on increasingly complex tasks as it proves itself. Key stages are:

1. Assisting developers: AI starts by offering suggestions in pull requests, highlighting potential issues, and helping refactor code under human supervision.
2. Executing small-scale tasks: As confidence in AI grows, it might be allowed to auto-merge safe refactors or format code without manual review.
3. Identifying and prioritizing work: AI transitions from reactive assistance to proactive contributions – detecting patterns of inefficiency, flagging outdated dependencies, or surfacing areas for refactoring.
4. Initiating tasks autonomously: The ultimate stage of trust – AI proposes and executes improvements, like optimizing database queries based on observed performance patterns, without human prompting.

A concrete example: At first, an AI assistant might only suggest reformatting code and catching syntax errors. Over time, as it demonstrates reliability, it could start suggesting small to medium-sized, non-controversial refactorings. Eventually, after proving alignment with team priorities, it might be trusted to make larger changes, such as optimizing functions for performance based on real-world usage patterns.

Each of these steps must be earned, not assumed. Teams need mechanisms to evaluate AI’s decisions, catch mistakes, and provide feedback – whether through human-in-the-loop (HITL) review systems, AI self-reporting, or explainability features that allow developers to verify AI’s logic before it acts independently.

Developing AI’s capabilities, and developers’ role in the process

For AI to move from a reactive assistant to an autonomous collaborator, it must develop specific abilities that enable deeper integration with development teams. Likewise, human developers will need to adapt their workflows to guide AI’s evolution effectively.

What do AI agents need in order to evolve towards greater autonomy?

To be trusted with higher levels of autonomy, we need to build certain abilities into AI agents:

• Memory: AI should retain context across interactions, learning from past decisions and refining its future recommendations.
• Pattern Recognition: AI should identify coding trends, architectural inefficiencies, and recurring issues, surfacing meaningful insights rather than isolated suggestions.
• Flagging uncertainty: AI must communicate its level of confidence in its decisions, highlighting when human validation is necessary.
• Self-Reflection: AI should periodically review its past actions, incorporating feedback and lessons learned into future tasks.

How can developers guide AI towards autonomy?

AI won’t develop these capabilities in isolation – it requires human guidance. Developers play a crucial role in refining AI agents by:

• Providing Clear Goals: AI needs well-defined objectives to ensure its decisions align with team priorities and broader project goals. At the beginning this could mean decomposing tasks further to make them fitting for agents to solve reliably.
• Offering Context: AI needs exposure to team documentation, code history, and past decisions to make informed suggestions. At the beginning this could take the form of developers providing explicit instructions and comments for agents to account for.
• Encouraging Inquiry: Developers should prompt AI to ask clarifying questions and intervene when its assumptions deviate from reality.
• Calibrating Behavior: Through iterative interactions, developers can shape AI’s decision-making process, helping it adapt to team-specific needs.
• Facilitating Reflection: Encouraging AI to analyze its past performance and adjust accordingly will reinforce productive behaviors.

If this sounds familiar it’s because these are some of the core mentorship behaviors¹. By fostering these abilities in AI and taking an active role in guiding its learning process, development teams can accelerate AI’s journey from a task-driven assistant to a proactive engineering partner.

A future where AI suggests, executes, and collaborates with your team

The promise of AI-native software development is about amplifying – not eliminating – human decision-making and teams’ impact. AI agents that earn autonomy can handle repetitive tasks, make informed suggestions, and even anticipate future technical needs, allowing developers to focus on complex problem-solving and creative work, as well as accelerate high-impact work.

But to get there, we must treat AI autonomy as a gradual, earned process, much like onboarding a new teammate. By establishing trust in competence and alignment with team goals, we can move beyond AI as a passive assistant and toward AI as an active engineering partner – one capable of identifying work, prioritizing improvements, and executing meaningful contributions.

For teams exploring AI adoption, the key question is: what’s the next step in building trust with AI? Whether it’s refining how AI learns from your feedback or gradually increasing its scope, the path to autonomy starts with intentional, incremental steps.