Agent Open Platform Architecture Design and Ecosystem Building Practices

1. Platformization: The Inevitable Path to Agent Value Delivery

Since around 2024, AI agent development across the industry has entered a fast-growth phase. Early efforts focused primarily on capability validation — equipping agents with basic task planning, tool invocation, and reflection-based error correction. As large language models have made significant progress in reasoning, instruction following, and multimodal capabilities, the model layer has crossed the practical usability threshold. The industry’s attention has shifted accordingly: how to bring agents into real business environments and deliver value that is stable, continuous, and auditable.

At this stage, three fundamental limitations of monolithic agent architectures have become increasingly apparent:

1. Closed capabilities: All tools and knowledge are embedded in a single system. Any new requirement must wait for official release cycles, leaving long-tail scenarios uncovered.
2. Fragmented scenarios: Government, education, finance, retail, media, and transportation each carry deep domain know-how. A single team cannot exhaustively master the business rules and compliance requirements of every industry.
3. Duplicated infrastructure: Authentication, credential management, task scheduling, and audit trails are repeatedly implemented across projects, resulting in low overall ecosystem ROI.

Platformization is the natural response to these problems. A clear product positioning has emerged across the industry: the goal is not to build “a more powerful tool,” but to build “a platform capable of hosting all tools.” This mirrors the relationship between operating systems and applications — the platform is responsible for kernel-level capabilities such as scheduling, memory, context management, and security governance, while scenario-specific capabilities across industries are delegated to ecosystem partners through standardized interfaces, enabling “integrate once, reuse across scenarios.”

2. Core Architecture: Five Capability Modules

Drawing on open platform practices that have been deployed in production, the capability layer is typically abstracted into five modules with clear boundaries:

Module	Positioning	OS Analogy
Expert	A specialized agent encapsulating domain knowledge and decision logic	Domain service
Skill	An atomic capability unit that models can invoke on demand	System call
Connector	Integration layer for external systems and third-party services	Device driver
Hardware	Access protocols for smart hardware and embodied AI devices	Peripheral bus
App	End-to-end solutions for end users	Application

2.1 Skills: Declarative Registration, Composability, and Governance

Skills are the smallest unit of platform openness, and their design quality directly determines the ceiling of the ecosystem. Three engineering principles are worth following:

• Declarative descriptions: Skill metadata must be understandable to models — under what conditions the skill is available, what the inputs and outputs are, and what side effects it may produce. The more accurate the description, the more reliable the model’s invocation decisions.
• Idempotency and retryability: Network fluctuations should not result in duplicate emails or duplicate signature requests. Skill implementations should provide idempotency keys or status query interfaces.
• Structured returns: Outputs should be machine-parseable to facilitate chained consumption by downstream skills.

An illustrative skill definition looks like this:

@skill(
    name="contract_sign",
    description="Initiate an electronic contract signing process and return a signing task ID",
    params={
        "doc_uri": "Storage address of the document to be signed",
        "signers": "List of signers, including names and contact information",
    },
    returns={"task_id": "Signing task ID, which can be used to query progress"},
    risk_level="high",   # High-risk operation: platform enforces human confirmation
)
def contract_sign(doc_uri: str, signers: list[str]) -> dict:
    ...

Fields such as risk_level are key governance levers for the platform: outbound messaging, payments, and legally binding actions should require mandatory human confirmation at the platform level, rather than relying on the discretion of individual developers.

2.2 Connectors: Bridging the “Last Mile” Across Heterogeneous Systems

The value of connectors lies in converging differences: whether integrating with internal enterprise office systems, mainstream international IM and collaboration tools, or government approval systems, all connections are unified under the same authentication mechanisms (OAuth 2.0, API keys, signature algorithms), credential management, rate limiting, and callback protocols. For ecosystem developers, this means “integrate once, use everywhere.” For the platform, it means security boundaries are centrally enforced.

2.3 Experts and Apps: Two Levels of Encapsulation

• Expert agents target judgment-intensive tasks — such as research topic evaluation, preliminary legal opinions, or investment research report generation. The core focus is defining knowledge boundaries and citation traceability; refusing to answer is preferable to fabricating sources.
• Apps target process-intensive tasks — such as unified service portals, course preparation, or store inspections. The core focus is end-to-end SOP orchestration and exception handling.

Both are built on the same foundation of “skills + connectors,” differing only in encapsulation granularity and target audience. Developers can choose which form to participate in the ecosystem based on their knowledge density and process length.

3. Three Technical Challenges and Engineering Responses

Launching an open platform is only the starting point. In real-world operations, three categories of problems have proven universal across the industry and require architectural design upfront.

3.1 Continuous Service Across Devices and Systems

A user drafts a proposal with an agent on an office computer in the morning, continues editing on a phone during the commute, and presents it on a conference room display — the task should not break when switching devices. This is a widely recognized challenge, requiring three engineering measures:

1. Externalized session state: Task orchestration state (execution position, intermediate artifacts, pending confirmations) is persisted server-side rather than in local memory. Any terminal acts merely as a rendering layer.
2. Tiered context synchronization: Full context and lightweight summaries are stored separately. Terminals on weak networks pull summaries on demand, balancing bandwidth and latency.
3. Checkpoint-based resume: Long-running tasks use checkpoint mechanisms to persist state at each stage, allowing recovery from the most recent checkpoint after process failure or device switching, rather than restarting from scratch.

3.2 Hallucination Control

AI hallucination is a shared challenge across the industry. An open platform must provide platform-level rather than application-level mitigation, because the governance quality of individual ecosystem developers will inevitably be uneven:

• Mandatory source tracing: Factual outputs must carry citation sources. Information without a verifiable source is not allowed into final answers.
• Tool-first policy: Information verifiable via APIs (inventory, exchange rates, schedules, contract status) must not be freely generated by models; it should always go through tool invocation.
• Dual-channel verification: High-risk outputs use cross-validation between a generation model and an independent verification model. If verification fails, output is downgraded.
• Confidence-based fallback: Low-confidence scenarios are routed to human handling. “Slow but correct” should be the default policy for enterprise-grade scenarios.

3.3 Task Stability

The longer the task chain and the more external dependencies involved, the greater the stability challenge. Key engineering practices include:

• Automated evaluation: Each skill maintains a minimal regression test suite integrated into the release pipeline. Every version update automatically runs the suite before rollout. Some platforms in the industry have sustained iterations of more than fifty versions within half a year, precisely because evaluation is front-loaded — high-frequency iteration and stability are not contradictory when automated evaluation connects the two.
• End-to-end observability: Every tool invocation’s input, output, latency, and exceptions should be logged, forming a replayable execution trace. This is indispensable for post-hoc auditing and issue localization.
• Failure classification and governance: Distinguish between model-caused, tool-caused, and network-caused failures, and define separate retry strategies and SLOs for each. For high-risk failures, explicit error reporting is preferable to silent suppression.

4. Ecosystem Operations and Developer Onboarding Practices

4.1 Standardized Onboarding Process

A typical onboarding process should form a closed loop: developer registration → sandbox development and debugging → submission for review (security scanning, least-privilege validation, use case verification) → canary release → full rollout. The key review criterion is consistency between declared permissions and actual behavior: a skill that claims to be read-only for calendar data should be blocked by dynamic behavior detection if write operations appear.

4.2 Tiered Partnership Models

• Industry partners: Participate as “Experts” or complete applications, contributing domain know-how in government, finance, education, healthcare, legal, and public welfare sectors.
• Tool partners: Participate as “Skills,” keeping offerings lightweight and focused. An electronic signature integration or a contract review capability can each be a standalone skill.
• Hardware partners: Join through hardware access protocols, bringing desktop terminals, robots, and other devices into the same task system so that embodied AI devices share the same scheduling and security framework.

4.3 Infrastructure Dependencies

The stable operation of an open platform depends on general-purpose cloud infrastructure: container platforms host skill runtimes with elastic scaling, message queues decouple asynchronous task chains, object storage persists task artifacts and knowledge bases, and logging services with monitoring and alerting support end-to-end observability. For example, general-purpose offerings such as object storage, message queues, container platforms, and logging services from providers like Baidu AI Cloud can cover the foundational needs of an agent platform across these dimensions, allowing teams to focus on capability abstraction and ecosystem governance.

4.4 Community and Scenario Depth

An ecosystem does not end at listing. Operationally, it requires continuous investment in developer communities, accumulating best-practice templates and FAQ knowledge bases. At the same time, dedicated editions for education, public welfare, and other public-interest scenarios should be introduced to lower adoption barriers for specific user groups — this is both an ecosystem responsibility and a source of scenario depth.

5. Emerging Trends: Three Directions Worth Watching

1. From desktop to embodied: Agent capabilities are extending from desktop productivity into the physical world. Once robots are integrated, the platform must address new requirements such as real-time constraints, physical safety boundaries, and offline autonomy. Hardware module protocol design needs to be forward-looking.
2. Globalization and compliance: When a product spans dozens of countries and regions and integrates with multiple overseas collaboration tools, cross-border data transfer, localized deployment, and compliance auditing must become first-class architectural concerns rather than afterthought patches.
3. Interoperability standards: Open standards for cross-platform skill description formats and credential management protocols are emerging. Early alignment can significantly reduce migration costs for ecosystem partners and enable the platform ecosystem to benefit from external network effects.

Conclusion

The essence of an agent open platform is to converge the complexity of “getting AI to accomplish something” into the platform kernel, while opening up the choice of “what to accomplish” to the ecosystem. For developers, understanding the five-layer abstraction of Expert, Skill, Connector, Hardware, and App — and mastering the three engineering pillars of continuous service, hallucination control, and task stability — means grasping the technical skeleton of a platformized agent system. For enterprise decision-makers, choosing an integration form based on their own knowledge density and process length is more pragmatic than pursuing an “all-capable agent.” The value of the platform lies in ensuring that every investment across the ecosystem can be reused and amplified.