Agentic Development

Agent harness: the foundation of AI-native software

AI-native software is software designed around models that reason, call tools, maintain context, and adapt interfaces while work is underway. Rather than attaching a chat box to an existing application, it treats agent coordination, multimodal input, long-running tasks, and human approval as core pr

MMichael SwindellAugust 19, 202616 min read

Agent harness architecture diagram showing six components: context assembly, tool access, progressive disclosure, state management, verification, and observability | Levr

AI-native software is software designed around models that reason, call tools, maintain context, and adapt interfaces while work is underway. Rather than attaching a chat box to an existing application, it treats agent coordination, multimodal input, long-running tasks, and human approval as core product primitives. For developers, that means designing the surrounding harness as carefully as the model choice: what an agent can see, which actions it can take, how it shares state, and how outcomes are verified. An agent-first control plane such as Levr provides one practical foundation by connecting project intent, tasks, tests, and review. The goal is not autonomy for its own sake, but reliable systems that make richer work possible.

At a Glance

Agents are useful primitives, but they are not the finished product category.

Core shift: AI-native products put models, context, tools, and verification at the center of the software design.
Critical layer: The harness around a model determines how it accesses data, selects tools, preserves state, and reports work.
New patterns: Long-running subagents, context compression, skill loading, dynamic interfaces, and conversational voice enable richer systems.
Engineering challenge: Teams need shared project context and observable workflows, not disconnected prompts and background jobs.
Human role: People still define intent, set constraints, evaluate outcomes, and approve consequential actions.

What makes software AI-native rather than AI-enabled?

AI-enabled software adds a model to an existing workflow, while AI-native software redesigns the workflow around model reasoning, tool use, persistent context, and adaptive interaction. The difference is architectural: the model is not a feature at the edge, but an active participant in how work moves through the system.

Many early AI products follow a familiar pattern: place a chat interface beside a conventional application, expose a few commands, and ask the model to assist. That can be useful, but it does not fundamentally change how the application represents work or how its systems coordinate.

AI-native software starts from different assumptions. Inputs may be text, speech, images, documents, application state, or live events. Actions may take seconds, minutes, or much longer. The interface may need to change based on what the system discovers. And the application needs a clear model of what an agent knows, what it is doing, and what must happen before a result can be trusted.

This is similar to earlier shifts in computing. A web page was an important primitive, but the web eventually produced search engines, marketplaces, collaborative applications, and mobile services. Likewise, a capable coding agent is a starting point. The more significant opportunity is software built around many interacting agent capabilities.

Why are agents best understood as a new software primitive?

Agents are best understood as a primitive because they combine language understanding, planning, tools, and execution into a reusable capability. Like web pages or spreadsheets before them, they enable new applications but do not define the final form of those applications.

A primitive is a basic building block that makes many later designs possible. Spreadsheets made calculations visible, editable, and broadly accessible. They did not eliminate accounting work. They expanded the amount and kind of work that people could perform with numbers.

Agents can have a similar effect. They can gather context, create plans, use APIs, execute bounded tasks, summarize results, and hand work back to people. That does not mean every workflow should run unattended. It means applications can delegate well-scoped work that was previously too costly or slow to coordinate manually.

The historical perspective matters because it discourages narrow product thinking. A conversational interface is not automatically an AI-native product. Nor is a background automation script. The design question is whether the product creates a coherent environment where intelligence, data, tools, interfaces, and human decisions can work together.

Vannevar Bush explored this broader idea of information systems that support human thought decades before modern AI in “As We May Think”. The specific technology changed, but the underlying challenge remains: build systems that make knowledge easier to navigate, connect, and act on.

What belongs in an AI agent harness?

An AI agent harness is the operational layer that gives a model relevant context, constrained tools, execution rules, memory, and feedback. It turns a general model into a system that can complete a specific class of tasks predictably enough for real software workflows.

Model selection matters, but it is only one component. A useful harness defines what the agent can access, when it should access it, and how it should use each capability. It also establishes the feedback loop that lets the agent check whether an action worked.

A practical harness usually includes the following:

Context assembly: Relevant project data, user intent, current state, prior results, and constraints.
Tool access: APIs, code execution, search, files, databases, communication systems, and other actions with clear boundaries.
Progressive disclosure: Tools and information loaded only when relevant, reducing unnecessary context and confusion.
State management: Durable records of tasks, decisions, artifacts, handoffs, and outcomes across sessions.
Verification: Tests, policy checks, review gates, or other evidence that confirms a result before it is accepted.
Observability: A traceable record of actions, tool calls, outputs, failures, and approvals.

The Model Context Protocol, or MCP, is relevant here because it provides a standardized way for AI applications to connect with external tools and data sources. The MCP specification describes a client-server protocol for this integration layer. A protocol alone does not solve context quality or governance, but it can make a heterogeneous agent environment easier to connect.

How should teams design shared context for long-running agents?

Long-running agents need shared context that is structured, durable, and selectively retrieved. Passing an ever-growing chat history is not enough because it becomes expensive, noisy, and difficult to audit as tasks, people, and agents interact over time.

Context is more than a prompt. In an engineering workflow, it can include the issue being worked, acceptance criteria, repository state, architecture decisions, related defects, test history, assigned owners, and approval requirements. The agent should receive the smallest useful set of this information for its present task.

That creates a need for context compression. A system can preserve detailed records while producing concise summaries for the next action. For example, a coding agent may not need every terminal command from a previous run. It may need a summary of the attempted fix, modified files, failing test, unresolved constraint, and recommended next step.

Shared memory is especially important when work crosses agent boundaries. One agent may investigate a defect, another may implement a patch, and a third may run validation. Each should work from the same authoritative project objects rather than reconstructing state from isolated conversations.

One way to handle this in Levr is to keep issues, acceptance criteria, test evidence, workflow gates, and attributed activity in one shared control plane. Agents can work against the same live task objects as human contributors, reducing the gap between what the plan says and what has actually happened.

Give coding agents context that survives the prompt.

Levr connects project intent, issues, tests, workflow gates, and agent activity so teams can coordinate coding agents without losing the shared state humans need to review and steer the work.

Explore Levr for agent-first engineering workflows

Why do AI-native interfaces need to be multimodal and dynamic?

AI-native interfaces need to be multimodal and dynamic because users communicate through more than text, and agents often need to present changing information, actions, and evidence. A fixed chat panel cannot always express a complex task, a live plan, or a decision that needs review.

Multimodality means treating text, voice, images, video, structured data, and application state as connected inputs and outputs. Modern systems can accept a spoken request, inspect a document, retrieve structured project information, invoke tools, and return a generated interface or concise summary.

Dynamic interface generation is particularly useful when the right interface depends on the task. An agent helping investigate a production incident may need to surface logs, timelines, service relationships, and mitigation actions. An agent helping plan a feature may need a scope proposal, dependency map, acceptance criteria, and a review decision.

The goal is not to generate every interface on demand. Stable workflows still benefit from predictable UI. Dynamic elements are most useful when a system encounters an unfamiliar problem, has to synthesize scattered information, or needs to expose the next best action clearly.

Voice is another important interface, especially for real-time systems. It supports conversational interaction, but it should remain connected to durable records. A verbal request to change a plan or trigger a task should create an inspectable state change, not disappear into an unsearchable conversation.

This shows up concretely in how AI-native products render themselves. A conventional application ships a fixed set of screens designed in advance. An AI-native application can generate a view it didn't have yesterday — a dependency map for an incident nobody anticipated, a comparison table for options a user just asked about, a form shaped by what the agent discovered mid-task. The interface becomes an output of the system's reasoning, not just a container for it.

This is a genuinely different design discipline than backend agent orchestration. A team can get context management, tool permissions, and verification exactly right and still ship a product that feels static and unresponsive if the interface layer never adapts to what the agent is actually doing. Dynamic rendering, voice input tied to durable state, and generated summaries are product decisions as much as they are technical ones — and they're where AI-native software most visibly diverges from a traditional application with a chat panel bolted on

How do you prevent agent orchestration from becoming uncontrolled automation?

Prevent uncontrolled automation by separating intent, execution, verification, and approval. Agents should have explicit task boundaries, limited permissions, observable actions, and quality gates that match the consequences of the work being performed.

Speed is not the same as reliability. A model can generate code quickly, but a team still needs evidence that the implementation meets the intended requirements and does not introduce regressions. This is why testing, review, and traceability become more important as agent throughput increases and it requires a person to approve actions with meaningful business, security, or production impact. This procedural guardrail matches the organizational changes modern groups undergo when redesigning dev team structures for AI coding speed.

Start by assigning autonomy according to risk. An agent can safely summarize a ticket or propose acceptance criteria with limited oversight. A task that modifies production configuration, accesses sensitive data, or merges code should have tighter permissions and a required review step.

A practical control checklist

Define the task contract: State the goal, scope, constraints, and acceptance criteria before execution begins.
Limit tool permissions: Give agents only the access needed for the assigned task.
Require verifiable outcomes: Link changes to tests, policy checks, or other concrete evidence.
Capture attribution: Record which agent or person took each action and why.
Use review gates: Require a person to approve actions with meaningful business, security, or production impact.
Learn from failures: Preserve failed attempts and their context so later work does not repeat the same path.

Levr supports this model by placing workflow states, quality gates, tests, issues, and activity history in the same project graph. Teams can choose manual, semi-autonomous, or more autonomous execution while holding human and agent contributors to the same definition of done.

How can engineering teams start building AI-native workflows now?

This lifecycle is exactly what a control plane exists to enforce. Levr's agentic workflow structures this loop directly — define intent, assign bounded agent work, verify automatically, and route exceptions to a human owner — turning the pattern below from a manual discipline into something the platform holds teams to by default. See how agentic software development works in practice for the full operational picture; here, the focus is on the underlying software design that makes each step possible.The first goal is not full autonomy. It is a dependable loop that creates useful evidence and compounds organizational knowledge.

A feature implementation workflow is a strong starting point because it already has recognizable artifacts: a request, requirements, code, tests, pull requests, and approval. Define how an agent should read and update each artifact rather than asking it to operate from a single unstructured prompt.

Define intent in natural language: Create a task with scope, constraints, acceptance criteria, and relevant links.
Assign bounded agent work: Let a coding agent investigate, plan, implement, or test within a clear permission boundary.
Capture proof: Record test results, changed files, pull request references, and a concise summary of decisions.
Review exceptions: Route ambiguous, risky, or incomplete outcomes to a human owner.
Improve the system: Turn repeated failures into better task templates, tool policies, tests, and retrieval rules.

That loop is more valuable than chasing an abstract notion of an autonomous developer. It connects a model's output to the systems that define engineering reality: requirements, source code, CI/CD results, quality signals, and accountable approval.

Technical Deep Dive FAQ

What is AI-native software?

AI-native software is an application designed with AI models as a core runtime component rather than an optional assistant. It gives models structured context, access to appropriate tools, and a way to execute and validate work. AI-native systems also preserve state across interactions, support human oversight, and may adapt their interface based on the task. The defining characteristic is that the overall workflow changes because model capabilities are part of the application architecture.

What is the difference between an AI agent and AI-native software?

An AI agent is a capability that can interpret goals, use tools, and carry out steps toward an outcome. AI-native software is the larger product and infrastructure environment in which one or more agents operate. It includes data models, permissions, context retrieval, interfaces, observability, verification, and human approval. A single agent can be useful on its own, but AI-native software coordinates agents and people across durable workflows.

What is an agent harness?

An agent harness is the software layer around a model that controls how it receives context, chooses tools, stores state, and evaluates results. It can include prompts, retrieval systems, tool schemas, policy rules, memory, retry behavior, and logging. The harness matters because a general-purpose model does not inherently understand a team's repository, workflow, access boundaries, or definition of done. Those operational details must be supplied and maintained outside the model.

Why is shared memory important for coding agents?

Shared memory prevents coding agents from treating every task as a fresh conversation. It lets them refer to prior investigations, architectural decisions, test results, unresolved blockers, and changes made by other contributors. For a team, the important requirement is not unlimited memory. It is reliable retrieval of the right project information at the right time. Durable shared context also creates an audit trail that helps engineers understand why an agent took a particular path.

How should an agent use tools safely?

An agent should use tools under explicit permissions and task-specific constraints. Read-only actions usually carry less risk than write actions, while production changes, external communication, and sensitive data access need stronger controls. Tool outputs should be treated as inputs that may be incomplete or misleading, and consequential actions should produce records that can be reviewed. Safe tool use combines least privilege, scoped tasks, observable execution, and verification after the action completes.

What is progressive skills loading for agents?

Progressive skills loading is the practice of exposing tools, instructions, or specialized capabilities only when an agent needs them. Instead of loading every possible API and policy into the context window at the start, the system can provide a focused set of skills based on the current task. This reduces unnecessary context and giving the agent a clearer action space. For a platform-specific application of this pattern using Anthropic tools, see our Claude Certified Architect exam guide.

How do long-running subagents coordinate work?

Long-running subagents coordinate work by operating against shared task state rather than relying only on a single conversation. A parent workflow can assign distinct roles such as research, implementation, testing, or monitoring, then collect progress and outputs as durable artifacts. Coordination requires clear ownership, handoff rules, and cancellation behavior. The system should also preserve summaries and supporting evidence so a later agent or human can understand completed work without replaying every intermediate step.

What should teams verify before accepting agent-written code?

Teams should verify that agent-written code satisfies the task's acceptance criteria, passes relevant automated tests, and fits the surrounding architecture and security expectations. A passing test suite is valuable but not always sufficient, particularly if requirements were unclear or coverage is incomplete. Review should consider changes to dependencies, permissions, error handling, observability, and deployment behavior. Linking test evidence and review decisions directly to the original issue makes the resulting work easier to trust and maintain.

Can dynamic AI interfaces replace conventional product UI?

Dynamic AI interfaces should complement conventional UI rather than replace it everywhere. Stable, frequent workflows benefit from predictable controls because users build familiarity and speed. Dynamic interfaces are most helpful when the system needs to synthesize a unique view of information, propose a task-specific plan, or expose an unusual decision. The best design often combines a dependable base interface with agent-generated summaries, forms, visualizations, and actions when the situation calls for them.

How does Levr support AI-native engineering workflows?

Levr provides a shared control plane for teams where humans and coding agents work together. Issues, acceptance criteria, tests, workflow gates, pull requests, CI results, and activity records can live in connected project objects. Coding agents can work through MCP while humans retain visibility and approval control. This approach gives agents more than an isolated prompt: it gives them durable project context and a place to record evidence as work moves toward completion.

Key Takeaways

AI-native software is not defined by a chat interface or a single autonomous agent. It is defined by the system around intelligent work.

The most useful next step is to build reliable loops, not theatrical demos.

Think in primitives: Treat agents as a foundation for new products, not the final product category.
Build the harness: Context, tools, state, permissions, and verification determine whether an agent can work reliably.
Preserve shared reality: Use durable project objects so people and agents operate from the same plan, evidence, and history.
Keep humans accountable: Match autonomy and approval requirements to the risk of each action.
Start narrow: Turn one real engineering workflow into an observable, testable human-agent loop before expanding scope.

The enduring opportunity is software that helps people coordinate more knowledge and more capable automation without losing the ability to understand and control the result.