Agent and Harness: The Boundaries and Future of Agent Engineering

In recent years, agent technology has undergone a clear shift from “showing what models can do” to “executing complex tasks.” Early discussions usually focused on whether a model had stronger understanding, generation, and reasoning capabilities. Now, more engineering practices are beginning to focus on another question: how should models be organized so that they can complete a real task reliably?

This is also the key to understanding the Harness.

In an agent system, the model is not the whole system. The model is responsible for understanding context, generating plans, and making decisions. The Agent is responsible for packaging model capabilities into a task-oriented execution unit. The Harness is responsible for providing the runtime framework: tool invocation, state management, permission control, the task loop, exception recovery, and result validation. Together, these three form the basic technology stack of modern agent applications.

1. Why Agent Interest Shifted So Quickly

Agents attracted attention not simply because models suddenly became “smarter,” but because the shape of applications changed.

Traditional model applications usually follow a one-request, one-response interaction pattern. A user asks a question, the model generates an answer, and the process largely ends within one turn. Tasks faced by agents often have the following characteristics:

1. The task goal is not fully clear.
2. External data or tools must be accessed.
3. Execution may last for a long time.
4. Intermediate results affect subsequent steps.
5. Plans must be revised continuously based on feedback.
6. The final result must be checked or verified.

For example, an automated software development task may require understanding requirements, retrieving code, modifying multiple files, running tests, analyzing errors, making further adjustments, and finally generating a change description. This is no longer simple text generation. It is a closed-loop system with state, actions, and feedback.

As a result, competition in the agent field has gradually shifted from “whose model has more parameters” to “who can organize models into a more reliable working system.” Once users begin to use products that can execute multi-step tasks, conversational experience alone is no longer the only evaluation standard. Task completion rate, recoverability, result quality, and operational safety become more important.

2. What Agents and Harnesses Solve

An agent system can be divided into four layers.

1. Model Layer: Understanding and Decision-Making

The model layer mainly provides language understanding, knowledge organization, reasoning, planning, code generation, and result summarization. It can propose the next action based on a task goal, and it can read information returned by tools and adjust its plan.

However, the model itself does not naturally provide capabilities such as:

• Direct access to internal enterprise data;
• Safe execution of local or remote operations;
• Long-term preservation of task state;
• Ensuring that every step complies with permission constraints;
• Determining whether a complex task is truly complete.

Therefore, the model is more like a decision core than a complete business system.

2. Agent Layer: Defining the Task Role

An Agent is an execution entity oriented toward a specific goal. It usually includes system instructions, a tool set, working memory, task strategy, and output specifications.

For example, a code analysis Agent may be asked to locate defects, generate modification suggestions, and run tests. A data analysis Agent may need to read data, generate statistical results, and explain anomalies. A customer service Agent needs to follow knowledge base, permission, and human-escalation rules.

The value of an Agent is that it turns a general model into a scenario-oriented work role. But without underlying runtime mechanisms, an Agent is still just a combination of prompts and several tools, and it cannot reliably handle complex tasks.

3. Harness Layer: Making Tasks Actually Run

The Harness can be understood as the runtime control layer of an agent. It does not necessarily produce content directly, but it determines how the agent works.

A complete Harness often includes:

• Tool registration and invocation;
• Parameter validation;
• Context assembly;
• Multi-turn execution loops;
• Task state persistence;
• Timeouts and retries;
• Permission and resource isolation;
• Recording of intermediate results;
• Output validation;
• Human confirmation points;
• Recovery or rollback after failure.

If the model is the “brain” and the Agent is a “staff member with responsibilities,” then the Harness is closer to the work environment, operating procedures, and process system. Without a Harness, it is difficult for a model to move reliably from “can answer” to “can complete.”

4. Infrastructure Layer: Reliable Operation

The lower infrastructure layer includes compute resources, container runtime environments, object storage, databases, message queues, logging systems, monitoring and alerting, and identity and permission systems.

These components do not directly manifest as intelligence, but they determine whether an agent system can run continuously, scale, and meet enterprise requirements. Especially in long-running and high-concurrency scenarios, task queues, state persistence, and observability are often more important than prompt optimization.

3. Will Models “Eat” Agents?

Stronger model capabilities will indeed compress the space for some simple Agents.

If an Agent is only a fixed template, simple question answering, or a small prompt wrapper, then once models have stronger context understanding and tool invocation capabilities, such wrappers can easily be replaced directly. They lack independent process assets and do not have runtime capabilities that are difficult to replicate.

But this does not mean Agents as a whole will disappear. A more reasonable judgment is that models will eliminate low-complexity Agents while increasing the value of high-quality Agents.

Agents with long-term value usually have the following characteristics:

• Deep understanding of a business process;
• Stable data and tool connections;
• Clear permission boundaries;
• Ability to handle exceptions and uncertainty;
• Responsibility for result quality;
• Ability to be monitored, evaluated, and continuously optimized.

In other words, future Agents should not just be “shells around models.” They should become reusable business execution units. Models provide general intelligence, Agents provide task structure, and Harnesses provide process control. None of the three can be omitted.

4. How General and Vertical Agents Divide Work

The advantage of general Agents is adaptability. They can handle many kinds of tasks without redesigning a process for every scenario. They are suitable for exploratory work, personal assistants, knowledge processing, and cross-domain collaboration.

The advantage of vertical Agents is specialization and controllability. They usually have clearer processes, data, permissions, and acceptance standards. They are better suited to scenarios with high accuracy and compliance requirements, such as finance, manufacturing, healthcare, research and development, and operations.

The two are not simply substitutes. They are more like an upper-and-lower-layer relationship:

• General Agents understand open-ended goals;
• Vertical Agents execute professional processes;
• The Harness restricts the scope of operations and records the process;
• Humans handle high-risk decisions and exceptions.

Future enterprise systems are likely to adopt a combination of “general planning + vertical execution.” The general layer breaks down tasks, the specialized layer invokes domain tools, and a unified runtime framework performs permission control and result aggregation.

5. Why Model Vendors Are Also Building Agent Products

There is a clear feedback relationship between models and agents.

On one hand, the model is the core source of capability for an agent. On the other hand, real task data from agents can expose model shortcomings. For example, a model may perform well in simple question answering but show goal drift, tool misuse, context loss, or unverified results in long-chain tasks.

Building agent products directly can help model research teams observe:

• Which tasks users truly care about;
• Which steps fail most easily;
• Which types of tool calls are most frequent;
• Which outputs require human correction;
• Where the model lacks stability;
• How training and evaluation can improve task completion capability.

This does not mean that models and applications must be built by the same company. It shows that there is strong engineering synergy between them. Models determine the upper limit of agent capability, while agents determine whether models can be converted into actual productivity.

6. Where the Opportunities Are in Agent Infrastructure

The opportunity in agent infrastructure should not be understood simply as “building another middleware layer that calls models.” Truly valuable directions usually concentrate where complexity grows rapidly.

1. Task Orchestration

When a task contains multiple steps, multiple tools, and multiple execution roles, a unified process orchestration system is needed. It should support serial execution, parallel execution, conditional branches, human confirmation, and failure retries.

2. State and Memory Management

Agents need to distinguish short-term context, long-term memory, task state, and external facts. If all information is stuffed into context, it is not only costly but also prone to information pollution. How to store, retrieve, and update information in a structured way is an important engineering problem.

3. Evaluation and Observability

Traditional applications can be monitored through API success rates and response times, but agents also need to evaluate plan quality, tool selection, step completeness, and final result accuracy. Therefore, task-level tracing, replay, and automated evaluation systems are needed.

4. Security and Permissions

Once an agent has execution capability, it may modify files, send messages, access data, or trigger business operations. Permissions should follow the principle of least privilege, and sensitive actions should add approval, sandboxing, and auditing mechanisms.

5. Cost and Resource Control

Long tasks may generate large numbers of model calls, tool accesses, and compute consumption. Systems need budgets, timeouts, concurrency limits, and degradation strategies to prevent a single task from looping indefinitely.

7. Multi-Agent Collaboration Does Not Mean More Roles Is Better

A common mistake in multi-agent systems is to simply split a complex task into multiple roles and let the roles talk freely. This can increase communication costs, context noise, and error propagation.

Effective multi-agent collaboration requires clarity on:

• The responsibility boundary of each Agent;
• The data structures for inputs and outputs;
• The conditions for task handoff;
• How conflict decisions are handled;
• The fallback mechanism after failure;
• Who validates the final result.

In many scenarios, a well-designed single Agent with reliable tools may be more stable than multiple Agents without constraints. Multi-agent systems are suitable for scenarios where tasks are naturally parallel, professional division of labor is clear, or mutual verification is needed, not for pursuing architectural complexity.

8. Core Evaluation Criteria for Agent Systems

Evaluating an agent should not depend only on whether its answer sounds natural. The complete task chain should also be observed.

Key areas include:

1. Task completion rate: Whether the goal is truly achieved, rather than merely generating plausible text;
2. Step efficiency: Whether there are many invalid calls and repeated attempts;
3. Result correctness: Whether the output has been validated against facts, format, and business rules;
4. Stability: Whether the same task performs consistently under different inputs;
5. Explainability: Whether key decisions and tool calls can be traced;
6. Security: Whether there is unauthorized access, data leakage, or dangerous operation;
7. Recoverability: Whether execution can continue or roll back safely after an error;
8. Unit cost: How much compute and human resource is required to complete one task.

These metrics together determine whether an agent is suitable for production.

9. What “Distilling Yourself into Skills” Means

In the future, individuals and organizations may turn experience into callable skill modules. A Skill is not just a few prompts. It should include:

• Applicable scenarios;
• Input format;
• Operating steps;
• Judgment rules;
• Tool permissions;
• Output specifications;
• Exception handling;
• Quality checks.

Once experience is structured, it can be reused by different agents. Individuals no longer only execute tasks; they can design task methods. Enterprises no longer rely only on a few experts; they turn expert experience into process assets.

However, distilling Skills does not mean removing the human role entirely. Tacit judgment, accountability, and value choices in complex business still require human participation. A more realistic trend is that humans shift from direct executors to process designers, result reviewers, and organizational coordinators.

10. Will AI-Driven Low-Headcount Organizations Appear?

Low-headcount organizations are already technically possible to some extent, but that does not mean “fully unmanned.”

An organization deeply driven by AI needs at least:

• A clear goal decomposition mechanism;
• Callable business tools;
• A unified task scheduling system;
• Continuous quality evaluation;
• Complete permissions and auditing;
• Human takeover for exceptions;
• A quantifiable cost and benefit model.

The real difficulty is not getting AI to complete a single task. It is keeping a large number of tasks running stably over time. Organizations must deal with accountability, data governance, legal compliance, customer trust, and system failures.

Therefore, in the short term, the more likely organizational form is “a small number of people managing a large number of agents,” rather than a company with no people at all. AI will significantly increase output per person, but it will not automatically eliminate the need for governance and decision-making.

Conclusion: Discussing the Harness Means Discussing a Productivity System

When we discuss the Harness, what we are really discussing is not a specific tool, but how to turn model capabilities into stable, controllable, and reusable productivity.

Models solve “can it understand and reason,” Agents solve “in what role is the task completed,” the Harness solves “how to make the task run reliably,” and infrastructure solves “how to carry and scale it over the long term.” The key to future agent competition is not only whether the model is stronger, but also whether the system can complete a closed loop:

Starting from goal understanding, moving through planning, execution, feedback, validation, and review, and finally delivering results in an auditable, recoverable, and scalable way.

For developers, the most worthwhile direction is not simply wrapping models, but going deep into task processes, tool systems, evaluation systems, and security boundaries. For enterprises, agent construction should not start with “connecting a model,” but with mapping business tasks, defining responsibility boundaries, and establishing observable runtime mechanisms.

As these foundational conditions mature, agents will truly move from demo applications to production systems and become an important part of the next generation of software engineering and organizational collaboration.