Chapter 4: Four Stages of Transition

The transition from traditional software engineering to AOSE progresses through four irreversible stages: Copilot Era → Agentic Era → Autonomous Collaboration Era → Autonomous Ecosystem Era. 2025 marks the critical transition period from Copilot to Agent modes, with human roles continuously migrating upstream in the value chain—from coding executors to strategic decision-makers.

1 Transformation Is Not Instantaneous

The transition from traditional software engineering to Agent-Oriented Software Engineering will not happen overnight. It is a gradual process of deconstruction and reconstruction, involving technology stack upgrades, organizational culture transformation, and shifts in mindset.

Understanding the characteristics of each transition stage helps us develop reasonable roadmaps and set realistic expectations.

2 Stage One: Copilot Era

Timeline: 2024-2025 (overlapping with the Agentic Era, gradually diminishing)

The Copilot Era is marked by the rise of tools like GitHub Copilot and CodeWhisperer, where AI acts as a programmer’s “pair programming partner,” completing line-level code completion based on local context (comments, function names).

However, as we enter 2025, this model is undergoing subtle yet profound changes. With the explosive maturation of Agentic tools such as Cursor Composer, Claude Code, and Kiro, developers have discovered that rather than having AI fill in the blanks within existing code, it is more efficient to have AI understand complete requirements and generate entire functional modules. Copilot-style “completion” has not disappeared, but is increasingly being replaced by more efficient Agent patterns in more and more scenarios.

// Copilot Era: Humans write comments, AI completes functions
// Calculate the number of days between two dates
public long daysBetween(Date d1, Date d2) {
    return ChronoUnit.DAYS.between(d1.toInstant(), d2.toInstant());
}

// Trend in 2025: Humans define technical requirements (implementation specifications),
// AI generates complete modules
// "Implement a reminder scheduler with three subtasks: scanning, generation, and sending..."
// Agent directly outputs: ReminderScheduler.java + ReminderTask.java + unit tests

2025 is the overlapping transition period between the two eras—Copilot remains in daily use, but more and more development tasks are migrating to Agent tools. The core task for teams is no longer “how to use Copilot well,” but rather how to gradually introduce Agent tools into daily work, transitioning from “assisted coding” to “task delegation.”

3 Stage Two: Agentic Era

Timeline: Became mainstream from 2025 (overlapping with Copilot Era, rapidly replacing)

Core Characteristics

Entering 2025, software engineering is undergoing a critical leap from “assistance” to “agency.” Agentic tools represented by Cursor Composer, Claude Code, Kiro, and GitHub Copilot Workspace are rapidly replacing the traditional Copilot model. These tools are no longer satisfied with “code completion”—they can understand complete requirements, autonomously plan tasks, generate multi-file code, and execute test validation.

A typical 2025 work scenario: A developer opens Cursor Composer and inputs “Implement user login functionality based on phone number and verification code, including verification code sending, validation, and token generation.” Within minutes, the Agent will:

1. Analyze requirements and design technical solutions
2. Create database schema and domain models
3. Generate backend API and frontend interface code
4. Write unit tests and integration tests
5. Submit a complete merge request

The core work for humans shifts to reviewing whether the Agent-generated solution aligns with requirements, rather than personally writing every line of code. This “task delegation” model is rapidly becoming the mainstream form of software development.

Notably: 2025 is the overlapping transition period between Copilot and Agent. Developers might use Copilot to complete functions in the morning and switch to Cursor to have the Agent generate entire modules in the afternoon. However, as Agent tool capabilities rapidly improve, scenarios for using Copilot are gradually decreasing.

Collaboration Mode: Professional VibeCoding

In the Agentic Era, the basic unit of human-machine collaboration leaps from function-level to task-level. However, unlike casual “VibeCoding” (improvisational programming) by non-professionals, professional engineers’ collaboration with Agents is a controlled conversational development—appearing as natural language exchange, but actually embodying rigorous task management and quality control.

Engineer’s VibeCoding Practice:

Professional engineers will not throw a grand requirement at the Agent all at once, but will deliberately control task granularity and context input:

Round 1: Engineer provides core context and initial task
"This is our project's user authentication module, tech stack is Spring Boot + MySQL.
Please first design the data model for the verification code service, considering
expiration mechanisms and anti-brute-force protection."

Round 2: After Agent generates the solution, engineer corrects course
"The data model is basically reasonable, but lacks frequency limiting.
Please add controls for daily sending limits and return user-friendly prompts
when the limit is reached."

Round 3: Refine the next subtask
"Model confirmed. Now implement verification code generation and storage logic,
requirements: 6-digit numeric code, 5-minute validity, stored in Redis."

Round 4: Check and proceed
"Implementation passes unit tests, but I noticed you didn't handle concurrency.
Please improve race condition handling to ensure the same phone number doesn't
receive multiple verification codes in a short time."

Key Control Points:

When collaborating with Agents, professional engineers maintain active control over the following dimensions:

1. Task decomposition granularity: Breaking down complete functionality into small tasks that the Agent can digest in a single session (typically 15-30 minutes to complete), avoiding too much context at once that leads to understanding deviation.

2. Context input management: Carefully selecting code files, configurations, and documents relevant to the current task as context for the Agent, excluding irrelevant information that could cause interference.

3. Process correction: When the Agent-generated solution deviates from expectations, promptly identifying issues and providing correction direction—“this should use transactions to ensure consistency,” “this query might cause N+1 problems, please optimize.”

4. Multi-dimensional result checking: Final verification includes not only “whether business requirements are correctly implemented,” but also:

   • Performance: Whether interface response times meet requirements, whether there are obvious performance pitfalls
   • Security: Whether inputs are validated, whether sensitive data is properly handled, whether there are injection risks
   • Maintainability: Whether code structure is clear, whether team coding standards are followed, whether it is easy to extend in the future

This collaboration style appears to be loose conversation, but is actually professional practice where engineers are requirements-oriented, constraint-bound, and quality-driven. The Agent undertakes the execution work of coding, while engineers always control the architectural direction and quality standards.

Core Changes: Role and Process Reshaping

Fundamental Transformation of Engineer Roles

In the Agentic Era, the core identity of software engineers undergoes profound change. They are no longer “the person who personally writes every line of code,” but transform into “the person who manages the code production pipeline”—responsible for defining production standards, monitoring production quality, and handling production exceptions. Specifically, the work focus shifts from “coding implementation” to “solution review”: reviewing whether Agent-generated technical solutions are reasonable, whether code logic is complete, whether business rules meet expectations, and whether edge cases are fully considered.

Systematic Reconstruction of Workflow

As Agents become the execution subject, traditional software development processes are also undergoing subtle changes:

• Requirements review evolves into requirements clarification: Team members no longer just review whether PRD documents’ feature descriptions are complete, but focus on whether these feature descriptions can be clearly expressed as product requirements and business requirements that Agents can understand.

• Task decomposition evolves into Agent automatic decomposition confirmation: Humans provide high-level task definitions, Agents automatically decompose subtasks based on their technical capabilities, and humans review the rationality of the decomposition and confirm the execution plan.

• Coding evolves into solution review: Humans no longer directly type keyboard to write code, but review Agent-generated code solutions to ensure they meet design requirements and quality standards.

• Code Review evolves into requirements alignment review: The focus of review shifts from superficial issues like code style and naming conventions to “whether this implementation truly satisfies the product requirements defined initially and the technical constraints in the implementation specifications.”

Key Deliverables and Milestones

Teams that successfully enter and pass through the Agentic Era will reap the following transformation outcomes:

• Pilot business modules achieve AI-led development: Teams can establish a complete working loop of “humans defining requirements, Agents autonomously implementing, humans reviewing and accepting” in one or more business modules, proving the feasibility of this model.

• Significant reduction in requirement delivery cycles: Since Agents can work 7×24 without interruption and without waiting for human developer context switching, requirement delivery cycles for pilot modules are expected to shorten by 30% to 50%.

• Emergence of the new “Agent Engineer” role: Team members particularly skilled at collaborating with Agents, proficient in requirements definition and constraint design, begin to emerge as core forces driving the transformation.

• Initial establishment of reusable constraint rule libraries: Teams accumulate verified technical constraints and business rules during practice, which can be reused in subsequent tasks to improve the accuracy and consistency of Agent generation.

4 Stage Three: Agent Autonomous Collaboration Era

Timeline: Ideal state (2028-2030+)

Core Characteristics: Maturation of Three Technical Prerequisites

The arrival of the Agent Autonomous Collaboration Era does not happen overnight; it is built upon the gradual maturation of three key technical prerequisites:

Prerequisite One: Significant improvement in Agent single-generation accuracy

As large model reasoning capabilities strengthen and domain-specific training deepens, the probability of Agents generating code that meets expectations in a single attempt significantly improves. In the Agentic Era, engineers need to frequently intervene to correct course, but in the Agent Autonomous Collaboration Era, Agents can “get it right the first time” in most routine scenarios. This means humans shift from “continuous supervision” to “sampling-based acceptance,” focusing more energy on requirements definition and value judgment.

Prerequisite Two: Multi-Agent fleets form collaborative closed loops

A single Agent’s capabilities always have boundaries, but “fleets” composed of multiple Agents can break through these limitations through role division and mutual verification. Typical working modes include:

• Architect Agent responsible for technical solution design and module decomposition
• Developer Agents (frontend, backend, database, etc.) responsible for code implementation in their respective domains
• Test Agent responsible for generating test cases and verifying functional correctness
• Review Agent responsible for code quality checks and security audits

These Agents form a “generate-verify-feedback-correct” closed loop: when the Test Agent discovers defects, it automatically feeds back to the Developer Agent for repair; when the Review Agent discovers security vulnerabilities, it triggers refactoring workflows. Humans only need to monitor the operation status of this closed loop, intervening to arbitrate when Agents cannot reach consensus automatically or encounter principled disagreements.

Prerequisite Three: Agents possess long-duration task execution capabilities

With breakthroughs in large model context control technologies (such as ultra-long context windows, key information extraction, memory mechanisms, etc.), Agents can handle complex tasks requiring continuous execution over hours or even days. Agents can maintain state during task execution, remember key decisions, proactively summarize when context is limited, and continue advancing without requiring humans to constantly provide context reminders. This enables Agents to undertake complete development of large projects, rather than only handling short, independent tasks.

Collaboration Mode: From Conversational to Delegated

When the above three prerequisites mature, the human-machine collaboration mode undergoes qualitative change. Humans no longer need to gradually guide Agents through multiple rounds of conversation, but can delegate complete business requirements in one go, with the Agent cluster autonomously completing the entire process from understanding to delivery.

Typical delegation scenario:

A product manager inputs to the system: “We need to add an expiration reminder feature for annual membership benefits, with the goal of improving renewal rates. Core logic: staged reminders at 30 days, 7 days, and 1 day before expiration, supporting multiple channels including App push, email, and SMS. The reminder 7 days before expiration should include a renewal coupon. Technical requirements: reminder delay not exceeding 5 minutes, interface performance meeting standards, compliance with SMS sending regulations.”

After receiving these business-level requirements, the Agent fleet automatically begins work:

1. Architect Agent analyzes requirements, designs service boundaries for “reminder scheduling-message pushing-coupon,” defines data models and interface contracts
2. Developer Agent cluster implements respective modules in parallel—scheduling Agent calculates reminder timing, pushing Agent integrates with various channel APIs, coupon Agent handles discount rules
3. Test Agent generates test sets covering normal flows, boundary conditions, and exception scenarios, automatically executes and reports results
4. Review Agent checks code quality, security compliance, and performance bottlenecks
5. If tests or reviews discover issues, automatically triggers correction loops until all acceptance criteria are met
6. Finally delivers a complete deployable solution to the product manager, accompanied by an intent satisfaction report

The human role in this process is: defining clear business requirements and acceptance criteria, monitoring the overall progress of the Agent fleet, making decisions on issues that the Agent loop cannot automatically resolve, and ultimately accepting whether delivered outcomes meet business value expectations.

Layered Collaboration Model: Autonomous Processes of Agent Fleets

In the Agent Autonomous Collaboration Era, the development process evolves into a highly autonomous system of Agent fleets:

Business requirements input (product manager defines goals and value)
    ↓
Agent fleet autonomous planning (Architect Agent decomposes, coordinates role Agents)
    ↓
Parallel development and mutual verification (multi-Agent collaboration, generate-test-review closed loop)
    ↓
Automatic iteration optimization (discover problem → automatically correct → re-verify loop)
    ↓
Delivery and intent satisfaction report (show results and verification basis to humans)

Humans no longer intervene in every execution step, but by setting clear requirements boundaries and acceptance criteria, allow Agent fleets to operate efficiently within autonomous scope. Human value shifts completely from “executor of how to implement” to “definer of what to do and why.”

Deep Restructuring of Team Structure

When Agent fleets become the main development force, human team role definitions undergo fundamental reshaping:

The essence of this restructuring is: humans shift from “participants in execution” to “commanders of Agent fleets,” responsible for setting battlefield rules, monitoring battle progress, and making decisions at key nodes, while specific tactical execution is entirely left to Agent fleets to complete autonomously.

Key Deliverables and Transformation Outcomes

When the three technical prerequisites mature and Agent fleets become the main development force, teams will welcome an order-of-magnitude improvement in software production efficiency:

• Agent fleets become standard configuration: 80%+ of coding work is automatically completed by Agent fleets, with humans completely shifting from “coding executor” to “Agent commander.” A single engineer can parallel-manage execution tasks of multiple Agent fleets.

• Extreme compression of requirement delivery cycles: From day-level delivery shortened to hour-level—when business requirements are clearly defined, Agent fleets can complete the entire process from solution design, code implementation, test validation to deployment within hours, with stable and reliable quality.

• Agent collaboration protocols become core assets: Compared to code repositories, “collaboration protocols” defining how Agent fleets divide labor, verify each other, and handle conflicts become more valuable organizational knowledge assets. These protocols determine the overall efficiency ceiling of Agent fleets.

• Fundamental reshaping of team structure: Traditional development team sizes may shrink by 50% to 70%, but overall team delivery capacity actually improves significantly. What remains are elite commanders most skilled at commanding Agent fleets, designing collaboration protocols, and making key decisions in complex scenarios.

5 Stage Four: Autonomous Ecosystem Era

Timeline: Ultimate form (2030+)

Core Characteristics: AI Proactive Suggestions, Human Strategic Decisions

The Autonomous Ecosystem Era represents the evolution direction of software system intelligence—AI not only possesses execution capabilities, but can proactively perceive business situations, identify improvement opportunities, and propose strategic suggestions to humans. However, this does not mean humans exit: humans are always responsible for defining What (what to do) and Why (why do it), and validating and deciding on AI’s suggestions.

A typical scenario: The system monitors rising user churn rates, AI proactively analyzes causes, generates multiple response strategies and prototypes, and proposes suggestions to the product owner—“Detected 15% increase in user churn rate, suggest adding personalized recommendation feature. Demonstration prototype generated, expected to improve retention by 8%, cost 300K, proceed?”

Key clarification: The product owner still needs to verify whether this “What” (doing personalized recommendations) truly aligns with business goals, and judge whether “Why” (to improve retention) accurately reflects the current core contradiction. AI provides analysis conclusions and suggestions; humans are responsible for confirming problem definitions and value judgments.

Requirements Granularity: Strategic Level

In the Autonomous Ecosystem Era, the unit of human-machine collaboration leaps to the strategic level. Humans set macro value directions (What and Why) and boundary conditions, while AI actively explores specific implementation paths and proposes them.

Human defines:
- What: Improve user retention
- Why: New customer acquisition costs have doubled, existing customer operations become key to growth
- Boundaries: Within 1M budget, show results in 3 months, without harming user experience

AI proposes:
- Option A: Personalized recommendation engine (expected +8% retention, cost 300K)
- Option B: Optimize onboarding guidance (expected +5% retention, cost 100K)
- Option C: Gamification incentives (expected +12% retention, cost 500K, medium risk)

Human decision: Verify whether each option's What aligns with strategy, evaluate whether Why holds, choose B+C combination

Human-Machine Collaboration: AI Proposes, Human Defines and Validates

Core human responsibilities:

• Define What and Why: Clarify strategic direction, business goals, and value pursuits
• Validate AI’s What and Why: Examine whether AI-proposed “what to do” hits the mark, whether “why” attribution is reasonable
• Value judgment and decision-making: Among AI-provided options, make choices based on business intuition and ethical considerations

Core AI responsibilities:

• Proactive analysis: Monitor data, identify problems, attribution analysis
• Generate solutions: Propose strategic options, build prototypes, predict effects
• Continuous optimization: After solution execution, continue monitoring, propose adjustment suggestions when necessary

In the Autonomous Ecosystem Era, on the surface AI assumes more “proactive” roles, but essentially this is a higher-level division of labor: AI is responsible for discovering opportunities, analyzing options, and proposing suggestions; humans are responsible for confirming whether problem definitions are correct (validating What), judging whether value attribution holds (validating Why), and making final decisions. In this mode, humans are liberated from tedious information collection and solution production, concentrating cognitive resources on value judgment and strategic choices that truly require human wisdom. Systems possess self-healing capabilities, product evolution is data-driven, but all of this ultimately requires human validation and confirmation of What and Why.

6 Comparison of Evolution Characteristics Across Stages

To more intuitively understand the evolutionary thread of the four stages, we summarize the characteristic changes of key dimensions as follows:

This comparison table reveals a clear evolutionary logic: as AI capabilities continuously improve, human roles continuously migrate upstream in the value chain, from specific coding implementation, gradually ascending to task management, requirements definition, and finally reaching the level of strategic decision-making. Each stage’s leap is not a negation of the previous stage, but capability upgrade and scope expansion built upon it.

7 Chapter Summary

This chapter depicts four progressive stages of transition from traditional software engineering to Agent-Oriented Software Engineering: the Copilot Era, the Agentic Era, the Agent Autonomous Collaboration Era, and the Autonomous Ecosystem Era. This evolutionary roadmap reveals the internal logic of AI capability development and human-machine collaboration model evolution.

Regarding this transition process, three key insights are worth emphasizing:

First, stage evolution has irreversible progressive nature. Each stage is a necessary foundation for the next; leaps are impossible. Without accumulated prompt experience and trust in AI capabilities from the Copilot Era, teams cannot effectively manage Agents in the Agentic Era; without practice in task decomposition and constraint design from the Agentic Era, high-quality three-layer requirements systems cannot be built in the Agent Autonomous Collaboration Era. The more solid the foundation laid in the previous stage, the smoother the ascent to the next stage.

Second, stage boundaries have overlapping nature and team differences. 2025 is the overlapping transition period between Copilot and Agent modes—at the tool level both coexist, but the frequency of Agent tool usage is rapidly rising. Specific to individual teams, some may already be fully using Agent tools, while others are still switching between the two modes, depending on business nature, technology stack, and team habits. The key is recognizing that this overlap is normal; the focus is not on “completely abandoning Copilot,” but on “gradually expanding the scope and depth of Agent tool usage.”

Third, clarifying current position and setting reasonable expectations is crucial. Teams need to honestly assess which stage they are currently in, concentrate resources and energy on core capability building for the current stage, rather than ambitiously pursuing concepts from the next stage. A team still in the early Copilot stage, if blindly pursuing “full-process intent-driven,” is likely to encounter setbacks. Conversely, if solid capabilities can be accumulated at the current stage, the leap to the next stage often happens naturally.

In the next chapter, we will shift from macro stage evolution to micro process reconstruction, specifically exploring how to implement development process transformation at the team level, grounding requirements-driven concepts in every aspect of daily work.

Discussion Questions

1. Comparing the characteristic descriptions of the four stages, honestly assess which stage you or your team are currently in? Are you just moving away from Copilot dominance, exploring Agent tools in the early Agentic Era, or have you established a relatively mature Agent collaboration model? Based on your current position, what should be the next most core capability building focus?

2. Stage evolution has irreversible progressive nature, but 2025 is the transition period between Copilot and Agent modes. Reviewing your coding work in the past month, what percentage was Copilot-style code completion versus Agent-style task delegation? Which types of tasks do you think are more suitable for Agent completion, and which still require traditional coding approaches?

3. In the Agent Autonomous Collaboration Era, “Agent Engineer” or “Agent Fleet Commander” will become core roles. Comparing the capability requirements of this role (task decomposition, constraint design, requirements definition), what do you consider your current strongest capability and biggest gap? How do you plan to bridge this gap?