AI 2022–2026: The Evolution of Multimodality and AI Agents

I. From Single-Point to System Capabilities: The Main Observation Thread

Between 2022 and 2026, the focus of artificial intelligence development was not the linear growth of single-model parameters, but the continuous evolution of how capabilities are organized. In the early stages, the industry focused heavily on pre-training scale, single-modal generation quality, and benchmark scores. Subsequently, technical routes gradually shifted toward two main tracks closer to engineering implementation: first, native multimodality became the default architecture for flagship models; second, AI agents evolved from “generating the next line of content” to “delivering an acceptable task.” These two trajectories jointly drove changes in evaluation methods, system architecture, and development workflows.

Looking at the timeline, on March 16, 2023, Baidu released its conversational large language model, signaling the start of a massive wave of LLM releases. Within the following two months, multiple vendors intensively launched conversational models, with public information indicating that up to 79 models with over 1 billion parameters were released. On February 15, 2024, a preview of minute-level video generation technology entered the public eye under the narrative of a “world simulator,” redefining people’s imagination of AI video and sparking widespread discussion in the creative industry. On March 12, 2024, the first product dubbed an “AI software engineer” appeared. It could read documentation, write code, run tests, and fix errors autonomously within a sandbox, pushing coding agents from “completing the next line” to “delivering a task.” The controversy over the gap between its demonstrations and real-world capabilities also defined the subsequent evaluation method for coding agents: rather than assessing whether the generated content looks good, the focus is solely on task completion rate.

II. Native Multimodality and Model Tiering: Architecture as Default

The core of native multimodality lies not in modeling text, image, audio, and video separately and stitching them together post-hoc, but in adopting a unified representation and alignment mechanism during the training phase. This allows the model to understand multiple modalities within the same context and perform cross-modal reasoning. In contrast, early multimodal systems often used a combined approach of a “vision encoder + text model + adapter layer.” While functional, this pipeline was complex, and cross-modal errors easily accumulated at module boundaries.

Once native multimodality became the default architecture for flagship models, its engineering impact manifested in four main areas. First, data pipelines shifted from single-modal cleaning to multimodal pairing, temporal alignment, and quality filtering. Second, model interfaces expanded from “text-in, text-out” to multimodal I/O, requiring unified abstraction. Third, inference scheduling must account for latency and cost differences introduced by images, audio, and video. Fourth, evaluation must cover cross-modal understanding, generation consistency, and safety auditing. Concurrently, the three-tier model classification—ultra-large, professional, and lightweight—has been widely adopted, indicating that the industry is gradually accepting the organization of model supply based on capability, latency, and cost. For developers, this means not just selecting the “strongest model,” but establishing model routing: complex tasks go to the high-performance tier, routine tasks to the professional tier, and low-latency scenarios to the lightweight tier, while retaining fallback and manual review mechanisms.

III. Dense Release of Dialogue Models: Shifting Competition to Application and Governance

The dense release of conversational models in 2023 ostensibly represented a growth in model quantity, but substantively reflected simultaneous changes in capability supply, deployment costs, and developer options. For developers, models were no longer scarce; what became truly scarce were scenario definition, data governance, evaluation systems, and delivery capabilities. The question enterprises needed to answer shifted from “Is there a model available?” to “Which model fits this task, how do we control costs, and how do we ensure output is auditable?”

This phase also exposed several engineering pain points: unstable model outputs, long-context information loss, prompt fragility, insufficient domain knowledge, and unclear safety compliance boundaries. Typical countermeasures include establishing task-level evaluation sets; adopting Retrieval-Augmented Generation (RAG) and tool calling to compensate for knowledge timeliness; using structured outputs to constrain formats; employing logs, tracing, and monitoring alerts to observe online quality; and introducing manual review for high-risk scenarios. General cloud-based services like object storage, message queues, container platforms, logging, and monitoring can support data management, asynchronous tasks, isolated execution, and observability. However, specific selections should be based on the enterprise’s existing architecture and compliance requirements, rather than centering around a single product.

IV. Minute-Level Video Generation: The World Simulator Narrative and Technical Challenges

The preview of minute-level video generation technology attracted attention because it advanced from “generating a few seconds of footage” to “generating minute-level content with temporal narrative,” using the “world simulator” as its narrative framework. For the creative industry, the potential value of such capabilities includes storyboard previews, material expansion, style exploration, and post-production assistance. For technical teams, the challenges are equally apparent: long-term temporal consistency, character and scene continuity, controllable editing, physical plausibility, compute costs, and content safety/copyright compliance.

In engineering implementation, video generation is typically not a synchronous API, but an asynchronous task: after submission, the task enters a queue, is scheduled by a worker process for generation, and the result is written to object storage before being returned following content review and version management. Development teams need to focus on task idempotency, failure retries, priority queues, cost quotas, and the traceability of generated results. More importantly, demonstration effects do not equal production readiness. It is necessary to establish business-goal-oriented evaluations, such as shot continuity, subject consistency, instruction adherence rate, and human adoption rate.

V. Coding Agents: From Completion to Task Delivery and Evaluation Shifts

The key shift in coding agents is upgrading from “code completion” to “task execution.” A usable coding agent typically needs to read documentation, modify code, run tests, parse errors, and iteratively fix them within an isolated sandbox. The difference between it and traditional completion tools is that completion optimizes local acceptance rate, whereas agents optimize end-to-end task completion rate. Because of this, the gap between demonstrations and real capabilities is quickly exposed, and evaluation must shift from “does the generation look right” to “is the task completed, are constraints met, and do regressions pass.”

A general pseudocode for evaluating task completion rate is as follows:

def evaluate_agent(agent, tasks):
    passed = 0
    for task in tasks:
        sandbox = create_ephemeral_sandbox()
        try:
            agent.run(task.prompt, workspace=sandbox)
            ok = (
                run_acceptance_tests(sandbox, task.tests)
                and check_constraints(sandbox, task.constraints)
            )
            if ok:
                passed += 1
        finally:
            destroy(sandbox)
    return passed / len(tasks)

In addition to task completion rate, one should also observe first-pass rate, average repair rounds, regression failure rate, average time consumed, cost per task, and auditability. Engineering requires sandbox isolation, least privilege, command whitelisting, network access control, change diff review, and comprehensive logging. For high-risk repositories, manual review and phased rollouts should be introduced to avoid mistaking “demonstrable feasibility” for “production reliability.”

VI. Engineering Insights and the 2026 Outlook

Overall, the evolution from 2022 to 2026 brings five key insights to development teams: First, evaluation first—define task success criteria before selecting models and orchestration methods. Second, task decomposition—break complex goals into verifiable sub-tasks. Third, isolated execution—agents must run in controlled environments. Fourth, observability—record inputs, tool calls, outputs, costs, and failure reasons. Fifth, layered governance—select model tiers and human review strategies based on risk, latency, and cost.

Looking ahead to 2026, multimodality and agents will further integrate: models will not only understand multiple inputs but also continuously plan and execute within tool environments. Evaluation will shift from single-turn Q&A to task-level, process-level, and system-level assessments. Safety compliance will expand from content review to permissions, data boundaries, and operational auditing. For developers, true competitiveness lies not in chasing single model names, but in building evaluable, fallback-capable, and governable task delivery systems.