The Agentic Shift 1: From Code Writers to Agent Orchestrators

Welcome to The Agentic Shift—a new series on the future of software engineering leadership.

As generative AI moves from novelty to necessity, developer workflows are undergoing a quiet revolution. The rise of intelligent agents is transforming not just how code gets written, but who writes it, how it's validated, and how teams are structured around it. In this first blog, we dive into the most immediate evolution beyond “vibe coding”: the emergence of multi-agent orchestration and what it means for engineering organizations. From shifting roles and success metrics to new governance models and performance frameworks, this series will explore how AI is reshaping the foundations of software teams—and what leaders must do to stay in front of the curve.

Beyond Vibe Coding: The Rise of Multi-Agent Orchestration

Ever wonder how AI companies like Anthropic and OpenAI ship new models and features so quickly? Their engineers aren’t just using GenAI to write code—they’re also supervising fleets of AI agents writing code and much more. At Anthropic, Claude Code operates as an agentic coding tool in the terminal and IDEs, with instances assigned specialised roles like architect, builder, and validator. These agents collaborate via shared planning documents, while engineers step in only to resolve conflicts or approve key decisions.

OpenAI’s newly launched Codex runs in cloud-based sandboxes, handling parallel tasks like writing features, debugging, submitting pull requests, and even citing logs and test results for traceability.

At Cognition Labs, engineers manage multiple instances of their AI software engineer, Devin, in parallel, with Devin already contributing 40% of code commits—projected to reach 50% by the end of 2025.

This isn’t just a productivity boost—it’s a structural transformation. Unlike cloud or DevOps, which changed how we deliver software, the agent-first shift redefines who builds it—and shifts engineering success from how much code is written to the outcomes that code enables.

And it’s happening faster than anyone anticipated. According to Steve Yegge at Sourcegraph, software development is progressing through six waves:

• Manual (2022): Traditional coding without AI assistance
• Completions-based (2023): AI suggests snippets, but devs stay in control
• Chat-based (2024): "Vibe coding" via conversational prompting
• Coding Agents (2025 H1): Autonomous agents executing code tasks
• Agent Clusters (2025 H2): Multiple agents working in parallel
• Agent Fleets (2026): Human supervisors managing entire systems of agents

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We’re entering a new era, where software engineering becomes less about writing code line-by-line, and more about orchestrating intelligent systems that build, test, and evolve software with minimal intervention.

Engineering in the Age of Orchestrating Agent Fleets

As coding agents evolve from helpful tools to autonomous collaborators, engineering is shifting from execution to orchestration. Developers won’t just write code—they’ll supervise systems of agents that plan, build, test, and optimise across the stack. This marks a deeper transformation: the substrate of software engineering is changing, and so is the role of engineering leadership.

To support this shift, agent architectures are evolving too. In some cases, we will see hierarchical systems—where a coordinator agent delegates to specialized agents for planning, implementation, and testing. In others, a single agent may handle tasks end-to-end. The structure will depend on agent competency, use case complexity and scale.

As foundation models improve, we’ll likely move toward centralised orchestration—where a single, capable agent manages specialised sub-agents, coordinating decisions, workflows, and resources across the system.

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This shift introduces a new set of imperatives:

1. From Prompting to System Planning

Success with AI agents isn’t about clever prompts—it’s about thoughtful, context-rich system planning. Agents perform best when they have a clear view of the current system and a well-defined outcome to aim for.

To enable this, teams must shift from reactive prompting to structured planning—defining goals, constraints, dependencies, and review criteria, often captured in “agent PRDs.” Tools like Cursor and Claude support this approach, helping engineers articulate not just what to build, but why, and how to break it into actionable steps.

As agent usage grows, planning becomes a critical service layer—one that may be supported by dedicated tools or even coordination agents managing strategy, context, and validation paths.

2. Rewiring Developer Intuition

Developers are used to bridging gaps from vague tickets to production-ready code by holding context in their heads. AI agents require more structure. To guide them effectively, engineers must externalise context through clear task definitions, workflows, and validation criteria.

This shift calls for new habits: thinking in plans, sequencing tasks, and writing evals to help agents confirm task success. It's a move from implicit expertise to explicit coordination—and it’s essential for scaling agent autonomy.

3. Redefining Excellence Metrics

As agents become active contributors in engineering workflows—already responsible for 40% of commits at Cognition Labs—new ways to evaluate their integration and impact become critical.

Traditional team metrics like deployment frequency and Mean time to recovery (MTTR) still matter. But to assess the effectiveness of agent-enhanced workflows specifically,  complementary metrics  are needed, such as:

• Agent-to-human contribution: What share of meaningful work is being completed by agents?
• Uninterrupted agent runtime: How long can agents operate autonomously without human intervention? (A leading indicator of maturity—like “miles without disengagement” in self-driving cars.)
• Agent transparency: How well can humans trace what the agent did, why it made certain choices, and how to adjust outputs when needed?

In agent-augmented teams, reviewing specs, interpreting agent decisions, and validating outputs becomes central to engineering excellence. These emerging metrics help track how effectively teams are integrating AI—not just how fast they’re shipping code.

4. Rearchitecting Governance for Scale

Autonomous agents fundamentally break traditional governance models. When hundreds of agents ship thousands of pull requests daily, manual code reviews become impossible to sustain.

At Anthropic, this reality is already evident. CPO Mike Krieger noted on Lenny's Podcast that with Claude generating over 70% of pull requests—often "larger than most people are going to be able to review"—traditional line-by-line reviews have become unsustainable. The team has shifted toward acceptance testing rather than detailed manual review.

As agent-produced code scales, the review process itself must evolve. While human oversight remains important, the volume and complexity of agentic output demand automated mechanisms for validation, benchmarking, and policy enforcement. Future-ready governance relies on embedded systems that assess correctness, intent alignment, and performance before code reaches production.

This requires a new governance architecture built around:

• Intent verification – ensuring agents align with expected behaviors
• Constraint enforcement – blocking unsafe actions at runtime
• Behavioral telemetry – tracing what agents did and why
• Change traceability – mapping outputs to agent decisions across time

Engineering leaders must treat governance as core infrastructure—allowing autonomy to scale safely without sacrificing control.

Agents Will Evolve Themselves — and Each Other

Today’s coding agents can execute tasks with remarkable autonomy. But the next frontier goes further: agents that not only perform, but also evolve. We’re entering an era where your AI development workforce will continuously improve itself, without manual intervention.

At TurinTech, we’ve been using evolutionary optimization techniques for years through Artemis—not just to fine-tune algorithms, but to refactor and productionize enterprise-scale codebases. The same principles apply to agent systems. Instead of relying on static instruction sets, agents will soon operate within closed feedback loops: testing, learning, and adapting to evolving codebases, system constraints, and performance targets.

What does this mean in practice? Imagine an agent that benchmarks its output, compares against performance baselines, and then mutates and re-selects improved strategies—day after day, sprint after sprint. Over time, this creates a self-optimizing software engine, where performance gains compound without requiring direct human intervention.

The Leadership Mandate — Preparing for the Agent-First Future

Agent-first engineering is not just a tooling upgrade—it’s a leadership shift. To stay ahead, tech leaders must rethink how they allocate resources, define excellence, and build teams.

• Budget Beyond Headcount: Your engineering capacity now includes agents. Budget for orchestration tools, compute, and validation infrastructure—just as you would for staff.
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• Redefine Performance: Traditional metrics won’t capture agent-driven output. Focus on agent-to-human contribution, supervisor efficiency, and production-grade stability.
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• Rethink Hiring: Junior roles won’t vanish, but expectations will shift. Prioritize system thinkers and agent supervisors. Upskill teams to manage and debug AI collaborators.
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• Govern with Intention: As agents gain autonomy, oversight becomes essential. Define escalation boundaries, ensure auditability, and embed governance into your workflows.
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• Move Early, Compound Fast: Early adopters will widen the productivity gap. Acting now builds institutional advantage—through faster iteration, deeper learning, and a more scalable development engine.

Lead the Shift, Don’t Chase It

Agent-first engineering is already reshaping how software is built. Developers are becoming orchestrators, and AI agents are taking on core development tasks.

The advantage will go to leaders who act early—those who invest in agent infrastructure, redefine team roles, and build for scale and governance from day one. Scale, speed, and precision now depend on how well you orchestrate—not how fast you code.

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