Large-Scale Multi-Agent Autonomous Engineering: Building an Operating System from Scratch

A Technical Observation: From Code Assistants to Autonomous Engineering Agents

AI coding capabilities in software engineering are evolving from single-line completion and function generation to long-horizon autonomous engineering for complex tasks. “Building an operating system from scratch” is a good example. The challenge is not generating a demo snippet, but whether the system can keep completing architecture design, module implementation, interface alignment, bug fixing, and a runnable deliverable without a human writing every line.

This kind of work usually relies on a large-scale multi-agent system. Separate agents handle planning, coding, testing, review, building, and regression. Task queues, logs, version control, and test platforms tie everything together into a closed loop.

For developers, what really matters is not a one-off “demo result,” but whether the system can be used reliably and repeatedly. With clear requirements, verifiable acceptance criteria, and strict sandbox constraints, it should be able to complete complex software engineering tasks. In other words, maturity should not be judged by whether the system has once produced a working system. It should be judged by whether the system can stay consistent, maintainable, and controllable across repeated tasks.

Why the Operating System Makes a Good Test Case

An operating system is a typical engineering target with strong dependencies, tight coupling, and hard constraints. It includes the boot process, kernel, memory and process management, device drivers, file systems, network stack, graphical UI, user-space tools, an installer, and a continuous testing system. A mistake in any one module can prevent the system from booting, corrupt files, break drivers, or deadlock processes.

For this reason, an OS exercises four kinds of multi-agent capability at the same time:

1. Long-horizon task decomposition: Can “build an operating system” be broken down into subtasks that can run in parallel and be verified?
2. Cross-module interface consistency: When modules are developed in parallel, do their interfaces stay unified?
3. A closed loop of build, run, and test feedback: Can agents fix the code automatically based on failure logs?
4. Fault localization and repair: When crashes, deadlocks, or driver failures occur, can agents keep iterating until the system converges?

Compared with isolated algorithm problems, an OS is much closer to real engineering. Requirements change, dependencies conflict, test failures must be traced, and performance bottlenecks need iterative optimization. That is also why an OS works well as a stress test for AI coding capabilities.

The Key to Multi-Agent Collaboration: Role Division and Information Architecture

Getting thousands of agent instances to work together cannot rely on free-form conversation, in which agents simply “chat with each other.” It requires engineered coordination. A common approach is to give agents clear roles:

• Architecture agents: Freeze interfaces, directory structure, and system-level constraints.
• Implementation agents: Code individual modules.
• Test agents: Generate test cases and run regression tests.
• Review agents: Perform static checks and standards audits.
• Build agents: Handle compilation, packaging, and release.
• Operations agents: Monitor resource usage and raise alerts on anomalies.

For the information architecture, a “shared bulletin board + task queue + event stream” pattern works well:

• Shared bulletin board: Global specifications live in a read-only knowledge base.
• Task queue: Task dependencies are written to a task queue.
• Event stream: Execution results are broadcast through an event stream.
• Searchable log store: Failure stack traces and logs go into searchable storage.

This setup prevents context from growing without bound. It also ensures that different agents work from the same shared facts.

From an Empty Directory to a Working System: A Typical Engineering Path

Take the example of building an operating system from an empty folder. The full path usually has six stages.

1. Requirements freeze

   Define the target hardware, boot method, supported storage media, file system, networking, graphics mode, and installation approach. If requirements are unclear, agents tend to drift in different directions.

2. Architecture design

   Define system call interfaces, the device abstraction layer, the process model, the memory layout, the build directory structure, and versioning rules. Frequent changes at this layer quickly multiply rework costs downstream.

3. Parallel module implementation

   The boot layer, kernel, drivers, file system, windowing system, and installer can be split along dependency lines and advanced by different agents in parallel. The split should minimize strong coupling between modules so that one module does not block overall progress.

4. Continuous integration

   After each code merge, automatically run compilation, unit tests, interface tests, boot tests, and image builds. Only with this automatic feedback can agents correct their code based on real results.

5. Fault injection and regression

   Simulate missing hardware, disk corruption, process crashes, network jitter, and other scenarios to test the system’s fault tolerance. The reliability of a real system is often less about the happy path working than about recovering from abnormal conditions.

6. Installation and rollback

   Produce a bootable image, provide an installer, and support failure recovery and version rollback. For an engineering system, being deployable, rollback-able, and verifiable matters just as much.

Evaluating Long-Horizon Autonomous Engineering: “Does It Boot?” Is Not Enough

The idea of generating an operating system “with one click” easily draws attention. From an engineering standpoint, however, a more complete evaluation framework is needed.

• Task completion: Does the result cover every acceptance item, not just a successful boot?
• Stability: Does repeated execution produce reproducible results?
• Defect density: Are crashes, deadlocks, memory leaks, and data corruption kept under control?
• Human intervention rate: How much code, configuration, or task goal does a person have to fix?
• Cost efficiency: How many compute and API resources are consumed per unit of valid output?
• Maintainability: Does the generated code have clear module boundaries, test coverage, and documentation?

Without these metrics, a complex task easily turns into a one-off result that “looks good in a demo but cannot be delivered consistently.” What engineering teams actually need is an automated production capability that can be reused, reviewed, and improved over time.

Beyond Operating Systems: Formal Proofs, Legacy Migration, and Hardware Co-Development

The value of multi-agent autonomous engineering is not limited to operating systems.

In formal proof, the task is to convert a natural-language mathematical proof into a formal representation that a machine can verify line by line. The work is tedious, repetitive, and error-prone, but it splits well into lemma proofs, proof strategy search, intermediate step completion, and final verification. Multiple agents can try different proof paths, the verifier can give feedback, and the agents can converge on a complete proof step by step.

In legacy system migration, agents can handle dependency analysis, interface mapping, equivalent implementation, test generation, and performance comparison. For example, when migrating an interpreted service to a compiled, system-level service, the focus is on startup time, memory usage, concurrency, and observability. Such migrations involve many repetitive checks and regression tests. This makes them well suited to agents handling the routine work while human engineers make the key decisions.

In hardware co-development, agents can also take part in driver development, simulation testing, firmware parameter tuning, and fault localization. This links software engineering with validation in the physical world. As toolchains mature, agents will no longer be limited to writing code. They will take part in the full engineering chain from design and verification to deployment.

Cost and Cost-Effectiveness: From Benchmark Runs to Cost per Valid Output

For an engineering team, model capability depends not only on generation speed or the price per API call, but also on end-to-end cost. A model may have a lower unit price, yet its total cost can still be higher if it has a high task failure rate, causes frequent rework, or requires constant human intervention. A better metric is therefore cost per valid output: the combined resources needed to complete one task that passes acceptance.

Measurable indicators include:

• Average API consumption per successful commit
• Number of iterations per bug fix
• Time spent diagnosing each build failure
• Share of work reviewed by humans
• Average task completion cycle time
• Maintenance cost of generated code once it is merged into the main branch

In scenarios that need higher throughput and lower cost, a high-concurrency agent cluster can shorten development cycles. It must be combined with strict sandboxing, access controls, and auditing to prevent gains in efficiency from creating security and quality risks.

Cloud-Based Engineering: Combining General Capabilities

To run a multi-agent engineering platform in production, you can build a closed loop from general cloud capabilities:

• Container platform: Hosts isolated agent runtimes and build sandboxes.
• Object storage: Holds code snapshots, build artifacts, log bundles, and test images.
• Message queue: Distributes tasks and smooths out traffic peaks.
• Log service: Centralizes retrieval of execution traces and exception stack traces.
• Monitoring and alerting: Detects resource limits exceeded, task backlogs, and rising failure rates.

With Baidu AI Cloud as the underlying resource platform, these general capabilities can be used to build an observable, auditable, and scalable pipeline for agent engineering. What matters is not piling on components, but giving every task a clear input, output, permission boundary, and rollback path.

Risk Governance: Capability Boundaries and Controllability First

Once agents can write code, run commands, modify configuration, or even manage infrastructure, a governance framework must be built at the same time.

1. Least privilege: Agents can access only the files, environments, and APIs their current task requires.

2. Environment isolation: Development, test, staging, and production environments must be strictly separated.

3. Human approval gates: High-risk actions must require human confirmation. These include deleting data, changing production configuration, publishing installation packages, and modifying security policies.

4. Change traceability: All code commits, task decisions, tool calls, and model outputs should be recorded in audit logs.

5. Automatic circuit breaking: When failure rate, resource consumption, or anomalous behavior exceeds a threshold, the system should pause automatically and notify the owner.

6. Capability boundary management: Operations related to self-improvement, privilege escalation, or changes to training pipelines should be disabled by default unless they pass a strict review.

Practical Advice for Engineering Teams

For teams that want to adopt multi-agent autonomous engineering, start with low-risk tasks that are easy to verify.

1. Start with clearly bounded tasks. Examples include code migration, test completion, documentation generation, API adaptation, and bug reproduction.

2. Build a high-quality evaluation set. Measure agent performance against real failure cases.

3. Adopt a “verify first, merge second” workflow. Any generated output must pass compilation, testing, and review.

4. Treat agents as engineering participants, not as a replacement for all engineering responsibility. Architecture decisions, acceptance criteria, security boundaries, and final releases should remain human responsibilities.

5. Keep building the knowledge base. Turn interface specifications, failure modes, test templates, and best practices into structured material that agents can read.

As task complexity grows, teams can gradually expand into system-level development, formal verification, infrastructure automation, and cross-module migration.

Conclusion

Building an operating system from scratch shows that AI coding is moving from local assistance to long-horizon autonomous engineering. Its core strength is not just generating code, but a combination of task decomposition, tool use, verification loops, cost optimization, and risk governance.

For developers, the worthwhile goal is not chasing impressive one-off demos, but building an engineering system that can be repeated, audited, and maintained. Only when multi-agent systems can consistently deliver high-quality results within clear boundaries will the way software is produced change in a meaningful way.