As model capabilities continue to improve, AI Agents are evolving from 'single-turn conversation assistants' to 'autonomous systems capable of running continuously for hours or even days.' However, engineering practice repeatedly proves that enabling a single Agent to steadily advance in long-term tasks is far more difficult than imagined. Issues like limited context windows, no memory between sessions, repeated rework, and misjudging progress can cause a complex project to completely lose control after just a few rounds of execution.
Just yesterday, Anthropic released a very important engineering solution specifically designed to address these challenges: a dual Agent architecture based on 'Initializer Agent + Coding Agent'.
Its significance lies in not combating problems with larger models or longer contexts, but through an engineered workflow design that ensures Agents can advance tasks step by step like human engineers, even under 'amnesiac' multi-window conditions.
Table of Contents
• Why Single Agent Architecture Fails Long-Term Tasks?
• Dual Agent Architecture: Making Agents Truly 'Work Like an Engineering Team'
• Initializer Agent: Laying the Engineering Foundation for the Entire Project in One Go
• Coding Agent: Do One Thing Per Round, But Do It Well
• How Dual Agents Solve Structural Challenges in Long-Term Tasks?
• Future Directions: From Dual Agents to 'Agent Engineering Teams'
Why Single Agent Architecture Fails Long-Term Tasks?
To understand the value of the dual Agent architecture, we must first understand why single Agents fail.
A long-term task, such as building a complete web application, often involves dozens to hundreds of features, multiple modules, continuous debugging, and verification. While current models are powerful, each round still operates within a limited context. This means every execution is a 'memory reset.' The model must quickly re-understand the project, assess the status, and decide the next action.
In this scenario, single Agents are prone to two typical problems:
• The first is trying to 'complete everything in one go.' Upon seeing user requirements, the model adopts an aggressive strategy: diving straight into large-scale coding until the context is exhausted. The consequence is it only produces 'half-finished code'—incomplete, unrecorded, untested. The next Agent round cannot determine project progress and wastes time figuring out the mess.
• The second is the opposite: the model sees partial results and mistakenly thinks the project is complete. Lacking a clear goal list and structured task definitions, the model may conclude 'fully functional' after seeing some UI, APIs, or responses, prematurely terminating the task. This 'premature completion declaration' is very common.
In other words, Anthropic believes the core issue preventing current AI Agents from stable long-term operation is not model capability, but the lack of a structured workflow that inherits task logic across contexts.
To this end, Anthropic proposed the dual Agent architecture.
Dual Agent Architecture: Making Agents Truly 'Work Like an Engineering Team'
Anthropic's solution is highly engineered: instead of one Agent handling everything, long-term tasks are split into two roles—one for foundation-laying, one for iteration.
This approach is very similar to the previously reverse-engineered Claude Code:
Its core idea:
The core logic of Claude Code is built on a single main loop. All historical messages are maintained in a flat message list, rather than a nested multi-agent conversation tree.
This time, Anthropic officially unveils this dual Agent architecture, namely Initializer Agent and Coding Agent.
Initializer Agent: Laying the Engineering Foundation for the Entire Project in One Go
The responsibility of Initializer Agent is focused on the first run; it acts more like a 'chief architect.'
It doesn't jump into coding immediately but converts the user's high-level requirements into a long-term maintainable engineering structure.
This includes three key components:
First, generating an 'actionable requirements system.'
Initializer doesn't let the model guess necessary features; it decomposes user requirements into a detailed JSON-structured feature list. Each feature has a description, steps, and acceptance criteria, all marked 'incomplete.' This allows Coding Agent in all future sessions to clearly know its goals, avoid misjudging progress, and not skip necessary steps.
Second, creating a state recording mechanism.
Initializer writes a progress file recording project structure, key notes, and handover context. It also sets up a git repository for commits, recovery, and tracking per iteration. This mechanism lets future Agents proceed based on facts, not guesses.
Third, providing a standardized startup script.
Initializer generates an init.sh to start the dev server and run basic tests. This enables quick verification of environment health in subsequent sessions, reducing the risk of 'discovering a broken project upon handover.'
Thus, a clean, structured, and sustainably inheritable engineering site is established.
Coding Agent: Do One Thing Per Round, But Do It Well
After initialization, project advancement is handed to Coding Agent. Its approach shifts from 'write as much code as possible' to 'incremental, reliable, verifiable modifications per round.'
Coding Agent's startup process resembles an engineer's first hour at work:
It first checks the current directory structure, reads git logs, views the progress file, runs init.sh, and performs a basic end-to-end test. This is not to implement new features but to confirm 'the site is normal,' avoiding work in unknown states.
Next, Coding Agent selects an incomplete feature from the list, reads its acceptance steps, and proceeds to implementation.
The key is:
It does only one thing per round.
After coding, it must perform end-to-end tests itself, e.g., using Puppeteer to drive the browser like a real user: open pages, click buttons, input content, observe results.
Upon passing, it writes "passes": true back to the JSON list, commits to git, updates progress file, so the next Agent instantly understands changes.
This pace is slow but extremely reliable.
It transforms 'unsupervised long-term tasks' into 'verifiable development iterations per round,' greatly improving task stability.
How Dual Agents Solve Structural Challenges in Long-Term Tasks?
This solution works because it addresses single Agent's insurmountable issues at the engineering structure level.
Here's a brief summary:
Challenge: Lack of task goal system; Single Agent: Prone to misjudging completion; Dual Agent: Feature list defines full requirement space.
Challenge: Cannot inherit state; Single Agent: Re-understands project every round; Dual Agent: progress + git ensures readable context.
Challenge: Prone to crashing halfway; Single Agent: Context explosion leaves unfinished work; Dual Agent: Coding Agent does one feature per round.
Challenge: Lack of real testing; Single Agent: Runnable code ≠ complete functionality; Dual Agent: Automated e2e tests ensure real performance.
Challenge: Environment may be damaged; Single Agent: Next round can't diagnose why; Dual Agent: init.sh unified self-check makes issues visible.
Essentially, this is the first time 'software engineering workflow' is formally encoded into Agent architecture.
Future Directions: From Dual Agents to 'Agent Engineering Teams'
Anthropic's implementation is an important milestone, but far from the end.
They mention next steps may further split roles into:
• Testing Agent
• QA Agent
• Code Cleanup Agent
• Documentation Agent
• Performance Agent
Forming true 'Agent engineering teams.'
This trend deserves close attention, as it may rewrite future software development.
Anthropic's scheme is not a model capability boost, but a breakthrough in engineering methodology. Combined with prior Claude Skills, etc., Anthropic is tackling real problems with engineering methods. However, some criticize that Anthropic merely documents encountered issues, even creating problems with MCP before solving them.
However, AI Agent development was never smooth; there are always issues, so even leading companies may step into many pitfalls repeatedly.