How should teams design agent harnesses so context does not inflate cost and degrade reliability?

Treat context as a budget, not a buffer. Load tool schemas on demand, compact history before the window is full, offload large tool outputs, and keep subagents on clean threads. The goal is to preserve the minimum state needed for the next decision, not to preserve every intermediate token. That approach lowers spend, reduces drift, and keeps long-running agents more predictable.

Designing the Harness Around the Context Window

Agent harnesses work best when the context window is treated as a scarce operating budget. Every token that stays in the thread has a carrying cost: more prompt processing, more latency, and more room for the model to drift from the current task. The harness should therefore decide what is needed for the next action, not what is historically interesting.

That means the harness should load tool schemas only when they are relevant, keep tool descriptions concise, and prefer structured state over free-form repetition. If a tool output is large, store it outside the thread and bring back a summary, a pointer, or the exact fields the next step needs. A long transcript is not a better transcript if it slows inference and increases the chance of stale assumptions.

Context compaction is most effective when it happens before the window is crowded. The harness should periodically compress older messages into task state, decisions, unresolved questions, and durable constraints. That preserves intent while dropping conversational noise. The practical test is whether the next step can still be executed correctly without re-reading the full history.

Keeping Tool Use Predictable as Context Grows

Reliability usually degrades when the harness blurs together unrelated state, tool chatter, and intermediate reasoning. A stronger pattern is to isolate each tool call, pass only the minimum required inputs, and store results in explicit slots rather than in the general chat history. That makes the system easier to reason about and reduces accidental dependency on incidental text.

Subagents should run on clean threads with clear task boundaries, especially when they are performing retrieval, transformation, or verification work. Reusing a noisy parent context for every subtask makes it harder to see which facts are still valid. Clean threads also make failures easier to diagnose because the subagent output is not entangled with unrelated prior turns.

When a harness depends on large tool outputs, it should normalize them into compact machine-readable records before reusing them. Raw logs, documents, and long API responses are useful evidence, but they are poor working memory. A harness that can cite, index, and rehydrate only the relevant slice will usually be cheaper and more stable than one that keeps everything live.

What Good Harness Design Looks Like in Practice

The strongest pattern is a separation between durable task state and disposable conversational context. Durable state should hold the current objective, tool permissions, known constraints, and the last trusted outputs. Disposable context should hold only the immediate reasoning surface. That split prevents the model from treating old text as active truth just because it is still visible.

Good harnesses also make state transitions explicit. When the task changes, the harness should say so in structure, not rely on the model to infer it from a long prose trail. When a tool result becomes outdated, it should be replaced, not left to age beside newer information. This is especially important in multi-step workflows where a slightly stale value can cascade into the wrong call or the wrong next tool.

For teams building these systems, the design question is not how to preserve maximum context, but how to preserve the smallest sufficient context with high confidence. That usually means tighter schemas, shorter prompts, structured summaries, and deliberate context refresh points. Those choices keep the agent cheaper to run and easier to trust over long sessions.

Standards & Framework Alignment

This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.

OWASP Agentic AI Top 10 addresses the attack and risk surface, while NIST SP 800-53 Rev 5 sets the governance and control requirements practitioners need to meet.

Framework	Control / Reference	Relevance
OWASP Agentic AI Top 10	ASI03 — Identity & Privilege Abuse	Context scoping and thread isolation affect how agents inherit authority and state.
	ASI08 — Cascading Failures	Context bloat can compound errors across long-running agent workflows.
Recommendation — Restrict each agent step to the minimum authority and context required for the action. Break long tasks into bounded steps to prevent error accumulation across agent chains.
NIST SP 800-53 Rev 5	AU-6 — Audit Record Review, Analysis, and Reporting	Compact state and explicit logs support reliable review of agent activity and outcomes.
	AC-6 — Least Privilege	Loading tool schemas and permissions only when needed reflects least-privilege access design.
	SC-23 — Session Authenticity	Clean threads and explicit task boundaries help keep sessions trustworthy and bounded.
Recommendation — Capture concise action logs so prior decisions can be reviewed without keeping full chat history. Grant each tool call only the permissions needed for the current task step. Bind each session to a clear task scope and refresh context when scope changes.

Practitioner Guidance

What to prioritise: Put budget controls into the harness itself. If context growth is uncontrolled, cost and reliability will degrade together, so the first fix is usually compaction and state separation rather than more model tuning.

What to verify: Check that the agent can complete the next decision from the compacted state alone. If it needs the full transcript to function, the harness has not yet separated durable state from conversational noise.

Common mistake: Teams often preserve too much raw output because it feels safer. In practice, that makes the thread noisier, increases token spend, and raises the odds that the model will anchor on obsolete detail.

What good looks like: The harness rehydrates only the fields needed for the next step, tool calls are narrowly scoped, and subagents can be restarted without inheriting irrelevant history.

Practitioner takeaway: A reliable agent harness is one that can lose most of its past and still make the right next move.