Inside the xAgent Agent Harness, Part 1: How Sessions Understand Task Changes

Many Agent systems describe a harness as one loop: send a user message to a model, execute the Tool Call returned by the model, and send the result back. That loop matters, but it does not answer an earlier question. When the user sends another message, does it continue the current task, begin a new phase of the same task, or fall outside the responsibility of the current Session?

xAgent handles this before the business Agent runs. The original input first enters the Session as a stable fact. The system then evaluates the task relationship, maintains Session goals, and adjusts Skills and Tools only when the change actually requires it. The business Agent receives an aligned task state and execution environment instead of a capability set permanently fixed when the Session was created.

DeepSeek Harness Task Orchestration and xAgent's Boundary

The official DeepSeek Harness site describes an Agent as the combination of a Model and a Harness, with models, Tools, Skills, Sessions, sandboxes, storage, loops, scheduling, and UI composed through plugins. Its official architecture documentation further describes the Session as an append-only event log and separates replaceable model adapters, Tool registries, Session components, and Agent Loops.

That means DeepSeek Harness task orchestration is not another name for attaching a few Tools to a DeepSeek model. It concerns the runtime that preserves Session facts, assembles capabilities, and keeps work moving across model calls. xAgent addresses the same system layer with a different ownership model:

Dimension	DeepSeek Harness	xAgent
Runtime composition	Plugin trees and profiles compose models, Tools, Skills, and runtime components	Each Session keeps the Skills, Tools, and runtime policy actually loaded for its work
Session continuity	An append-only Session event log supplies context	SessionEngine owns history, phase and Session-wide goals, and runtime state
Orchestration entry	Agent, Session, and plugins form a replaceable execution path	A new input is compared with the current phase and Session goal before capabilities change
Primary boundary	Contracts between plugins and core runtime components	Fact ownership across Brain, SessionEngine, and the business Agent

Both designs treat the model as a participant while the Harness owns continuity and capability assembly. The difference is emphasis: DeepSeek's public design centers on a plugin-composed runtime, while this article explains how xAgent interprets a task change before its business Agent starts. xAgent is not a DeepSeek Harness fork or frontend; this is a factual comparison of how two systems approach the same architecture problem.

Why a Tool Loop Is Not Enough

Suppose a Session is working on “research ComfyUI and deliver a Markdown guide.” The user's next message could take three forms:

• Continue: it is still the current phase.
• Convert the Markdown we just created to Word: the long-term goal remains relevant, but the delivery phase has changed.
• Also check tomorrow's weather: the request is outside the responsibility of this specialist Session.

If the system extracts keywords only from the latest sentence, Continue contains almost no useful semantics. If every message causes a complete capability reselection, ordinary refinements create unnecessary environment churn. If the latest message simply overwrites the old goal, a specialist Session gradually loses its original responsibility.

xAgent therefore separates two questions: “How does this message relate to the current task?” and “What capabilities does the resulting task require?”

Two Goal Levels Describe Session Work

An execution Session keeps two distinct goal levels:

Goal	What it represents	When it changes
Session-wide goal	The business scope and expected result this Session owns over time	When the Session's responsibility truly changes
Current-phase goal	The step or delivery phase currently being advanced under the wider goal	When work enters a new phase or the current work is refined

For example, “research ComfyUI and deliver a complete guide” can be the Session-wide goal, while “document node connections” is the current phase. When delivery changes to Word, the current phase can change while the Session remains responsible for the same research task.

These goals are stable Session runtime facts. A context summary may refer to them to preserve continuity, but it cannot overwrite them. Complete Plan and Task structures also remain with their own fact owners instead of being copied into Session goals.

The Original Input Must Become a Fact First

xAgent does not evaluate relevance as soon as a message reaches a Channel. The message first enters the Session queue. Brain acquires the single execution right for that Session, SessionEngine binds the next input in actual consumption order and appends it to history, and only then does semantic evaluation begin.

User or Session collaboration input
  -> SessionEngine queues and preserves the original input
  -> Brain acquires the Session's single runner
  -> SessionEngine binds the next task input
  -> Evaluate the task relationship
  -> Align task state and the capability environment
  -> Enter the business Agent loop

This order solves two problems. First, a supporting-model failure cannot make the user's original message disappear. Second, when several messages reach one Session quickly, each one is evaluated against the task state committed by the previous input rather than the stale state that existed when the message first arrived.

Approval decisions, Tool results, system notifications, and deterministic commands do not enter this relevance path. They continue through their existing approval, execution, or control paths instead of being misclassified as new tasks.

The Relevance Agent Produces Signals Only

xAgent uses an internal, stateless task-relevance Agent. It receives only:

• the current original user message and stable resource references;
• the current-phase goal;
• the Session-wide goal.

It does not receive the full History, candidate Skills, candidate Tools, Memory, execution steps, or an existing orchestration result. Its output contains only two scores: relevance to the current phase and relevance to the Session-wide goal.

The relevance Agent does not generate a new goal, recommend Skills, decide whether to compress context, or modify the Session directly. It converts a semantic comparison that string rules cannot handle reliably into a constrained runtime signal. Brain still selects the state transition using the scores and deterministic runtime state, and SessionEngine commits the resulting business action.

How Two Dimensions Select the Next Path

Readers do not need to depend on exact numeric thresholds. High, low, and uncertain bands create the following decision boundary:

Current-phase relevance	Session-goal relevance	Default interpretation	System behavior
High	High	Continue, refine, or correct the current work	Keep the task and capabilities, then continue
Low	High	A new phase within the same responsibility	Update the phase, reconcile capabilities when needed, and evaluate whether the previous phase can be compressed
Low	Low	A new task or an out-of-scope request	Do not overwrite the current goal or capabilities; preserve the request for responsibility routing
High	Low	The phase and Session-wide goal may conflict	Preserve the current state instead of replacing the goal automatically
Uncertain	Any	The signal is not strong enough for a state change	Preserve context and capabilities; let the business Agent reason or ask the user

The scores are not business facts and are not written into Session goals or context summaries by default. This allows the scoring model and thresholds to evolve without turning one model judgment into permanent state.

Task State and the Capability Environment Change Separately

A task change does not necessarily require the execution environment to be rebuilt. If the user corrects wording, adds a constraint, or asks the Agent to continue, the existing Skills and Tools usually remain appropriate, so xAgent keeps them.

Only initial task setup or entry into a new phase enters the environment-preparation path:

Aligned complete task
  -> Extract task terms for retrieval
  -> Recall Skill, ToolSet, and Memory candidates
  -> Orchestrator selects the target capabilities
  -> SessionEngine applies a differential Skill and Tool update

The following customer-support reporting run shows the environment-preparation stages as separate runtime states: task-essential extraction, Skill, Tool, and Memory discovery, and orchestration.

Task terms are used only for candidate retrieval. They are not passed into Orchestrator beside the complete task as a competing task description. Reconcile mode also carries the current capabilities, so Orchestrator can retain selections that still apply and change only the target environment difference. It consumes one normalized task, the current capabilities, and the retrieved candidates. It does not create Sessions, rewrite task goals, or select the model or Agent identity.

This separation prevents three model roles from overwriting one another. The relevance Agent compares message relationships, task-semantic extraction supports retrieval, and Orchestrator selects capabilities. SessionEngine remains the owner of Session facts.

The resulting runtime environment remains inspectable after orchestration. In this run, the Session loaded planning, capability-discovery, Session-collaboration, and file tools needed to execute and verify the report:

The Receiving Session Prepares Its Own Environment

The newer path also changes sub-Session creation. The main Session determines whether an appropriate responsibility Session already exists. When a new Session is needed, session_create_sub creates a minimally runnable Session with deterministic defaults and transfers the original task message and resource references.

The creating Session no longer interprets the task on behalf of the receiving Session, selects its business Skills and Tools, or generates an execution Prompt before creation. The sub-Session receives the task through the same task-control loop as an ordinary user input, then initializes its own goals and capability environment.

This establishes a stable rule: the Session that executes the task is the Session that understands it. It also prevents an orchestration failure from blocking Session creation and transfer of the original task. To learn how Sessions exchange tasks and materials, read Multi-Agent Session Event Collaboration.

Supporting Judgment Must Not Take Over the Main Path

Task-relevance evaluation is a degradable pre-execution capability. If the call fails, the output is invalid, or the scoring model is temporarily unavailable, xAgent treats the situation as “not enough evidence to change the task.” It preserves the goals, capability environment, and compression state, then continues processing the saved user message.

Capability reconciliation failure is a different problem from relevance failure. The original input and committed task state must not disappear, and existing capabilities must not be cleared before a replacement environment has been selected successfully. Each failure remains inside its own responsibility boundary while recoverable facts stay intact.

What Users Can Observe

When a message simply continues the current work, users may briefly see task-semantic understanding before execution continues. When a message starts a new phase and requires different capabilities, the timeline can show a sequence similar to this:

Understanding the task meaning
  -> Extracting task terms
  -> Discovering task capabilities
  -> Orchestrating the task
  -> Applying the task environment

These notices describe the stages currently advanced by the Harness. They are not model-generated chat content, and they do not imply that every message runs every stage.

To learn how capabilities enter a Session on demand, read How AI Agents Discover and Load Tools and Skills on Demand. For the boundary at which model, Skill, and Tool changes affect later calls, read Runtime Model and Skill Switching.

Next: How Aligned Work Keeps Running

The task-control loop answers “What task does this message belong to, and what environment does it need?” Once that environment is ready, the business Agent enters the fixed model and Tool execution loop.

The next article, Inside the xAgent Agent Harness, Part 2: How Tasks Run, Pause, and Resume, explains how the single runner, context assembly, Tool Calls, approval waiting, context compression, and restart recovery form one execution path. For the user-side workflow, you can also read How AI Agents Run Long Tasks.