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Reliable long-horizon agents need more than a large context window: they need durable state between sessions, traces that expose where runs go wrong, checks against the real environment, and recovery that accounts for both agent memory and external side effects. Token burn is workload-specific, so measure it in your own runs rather than relying on a supposed typical rate.
Why long-horizon execution needs explicit controls
A multi-step agent can span separate sessions, context windows, tools, and environment states. If a run fails, the final response alone may not reveal whether the cause was a mistaken decision, a tool error, stale context, an external state change, or a failure to verify completion. If it succeeds once, that does not establish that it will succeed reliably.
Anthropic’s engineering article, “Effective harnesses for long-running agents,” published November 26, 2025, describes cross-window consistency as an open problem: “However, getting agents to make consistent progress across multiple context windows remains an open problem.” Its example uses an initializer to prepare work and leave durable artifacts for later sessions. That is a documented design example, not a universal recipe or a guarantee that compaction alone will produce consistent results.
What must persist across a session boundary
When one session hands work to another, the next run needs a trustworthy account of the task and the current state. As a practical design recommendation, persist the information needed to resume and verify work rather than relying on a summary that merely says what the previous agent intended to do.
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- Task definition: the requested outcome and the requirements that determine whether it is complete.
- Current state: what has changed in the project or external environment, with references to the relevant files, records, or other evidence.
- Completed and remaining work: distinguish verified completion from attempted actions and unresolved requirements.
- Decisions and constraints: record consequential choices, assumptions, and restrictions that a new session should not silently reverse.
- Recovery information: note any partial or uncertain side effects that need checking before the agent retries.
Anthropic’s example artifacts include a feature list, setup script, progress log, and initial commit. The transferable idea is to make handoff state inspectable and actionable. On resumption, have the agent verify the relevant state against the environment before continuing; a progress note is a lead for that check, not proof that the described change still exists.
Instrument the trajectory, not just the final answer
A useful run record should let an engineer reconstruct what the agent received, what it tried, what tools returned, and where the run first became unrecoverable. Capture enough context to connect tool calls and observations to the task, while following the system’s privacy and data-retention requirements.
- Record the task and the model and harness configuration used for the run.
- Keep the sequence of agent actions, tool inputs, tool outputs, and relevant environment observations.
- Mark errors, retries, context transitions, and the point at which the run stopped or was judged unsuccessful.
- Link the trace to an outcome check, so an engineer can compare the claimed result with the environment state.
Microsoft Research’s AgentRx benchmark contains 115 manually annotated failed trajectories (Microsoft Research, 2026) and frames diagnosis around the execution trajectory and its critical failure step. That benchmark illustrates why finding the decisive point in a run can be more useful than labeling the whole run “failed”; its count describes the benchmark, not a universal failure rate.
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For multi-agent diagnosis, TraceElephant reports that full execution traces improved failure-attribution accuracy by up to 76.5% against its partial-observation counterpart (Association for Computational Linguistics, 2026). This is a benchmark-specific result, not a promised production improvement. In practice, trace completeness is a trade-off: collect the observations needed to diagnose failures, but avoid retaining sensitive content without a clear operational reason.
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Verify outcomes in the environment
An agent’s final natural-language statement is not sufficient evidence that a task succeeded. Anthropic’s evaluation guidance gives the example of a booking agent: saying a reservation was made does not establish that a reservation exists in the database. Evaluation should inspect the resulting environment state against the task requirements and retain the run interactions needed to explain the result.
For a long-horizon task, define the success check before running the evaluation. For example, a task may require a record to exist with specified fields, a file to contain an expected change, or a workflow to reach a particular state. The check should inspect that state directly where possible, and distinguish partial completion from full completion. If the outcome cannot be independently observed, report that limitation instead of treating the agent’s assertion as verification.
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Evaluate non-deterministic behavior across repeated runs
A single successful run is weak evidence of reliability when the same task can produce different trajectories. As an operating recommendation, run repeated trials and report the task, harness, model and configuration, environment, and outcome definition alongside the results. The sources cited here support trajectory-level analysis and benchmark evaluation, but do not establish a universal number of trials or a reliability threshold.
When a run fails, inspect the earliest decisive failure rather than counting only the final outcome. Separate failures such as an incorrect plan, a tool or environment error, a bad handoff, and a missing outcome check. Those categories are a practical diagnostic scheme, not a failure taxonomy established by the cited benchmarks. The distinction matters operationally: retrying may be reasonable after a transient tool error, but repeating the same action after an incorrect assumption or an unverified side effect may compound the problem.
Design recovery around both context and environment
Recovery has two state dimensions: what the agent believes has happened, and what has actually happened in the environment. Restoring only one can leave the run inconsistent. If context is restored but the external action already took effect, the agent may repeat it. If environment state is restored but the agent retains a later, inaccurate account, it may proceed from a false premise.
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AgentRewind proposes recording aligned checkpoints of agent context and controlled environment state so execution can return to a prior point and resume after an error. Anthropic’s managed-agent engineering account describes a different operational pattern: separate the harness, session log, and sandbox so the harness can surface a container tool-call error and provision a replacement environment for a retry. These are documented approaches, not evidence that one recovery design is best for every system.
Before retrying after a failure, check whether the action may already have changed an external system. Where an action cannot be rolled back, a compensating action or human review may be appropriate design choices; the sources here do not establish one prescribed compensation protocol.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Include long-horizon security in evaluation
Security evaluation should consider what an agent can be induced to do over multiple turns, not only how it responds to an isolated input. AgentLAB covers five attack types across 28 environments and 644 security test cases (Proceedings of Machine Learning Research, 2026). These are the benchmark’s scope figures, not a measure of real-world attack frequency or a guarantee that a system tested against it is secure.
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For teams, the operational implication is to include multi-turn and long-horizon scenarios in the evaluation plan and inspect the trajectory and resulting state. A final answer that sounds safe does not establish that the agent took no unsafe action along the way.
Measure token burn per successful task
The sources discussed here do not establish a general token cost per long-horizon run, a typical retry overhead, or a token saving from summaries, context resets, or recovery. Treat token burn as a property of a particular workload and configuration, and measure it in your own traces.
As a practical measurement recommendation, record input and output tokens, retries, context-management operations, tool calls, successful task completions, and monetary cost. Report token totals separately from currency: model and service prices and configurations can change, and equal token counts do not by themselves establish equal monetary cost.
A useful operational comparison is cost per successful task: total measured cost for the evaluated runs divided by the number of runs that met the defined success check. State the workload, run count, task success definition, model and service configuration, measurement date, and whether failed and retried runs are included. Compare like-for-like runs; otherwise a change in task difficulty, success criteria, or included retries can look like a cost improvement when it is not.
Turn the controls into an operating loop
- Define completion: state the required outcome and how it will be checked in the environment.
- Prepare durable handoff state: keep the task, current state, completed and remaining work, and consequential decisions available across sessions.
- Capture the execution: retain relevant actions, tool results, errors, transitions, and outcome evidence for diagnosis.
- Verify before continuing or declaring success: check the environment rather than trusting a progress note or final response alone.
- Recover deliberately: establish whether external side effects occurred, restore aligned state where available, and choose a retry, compensation, or review path.
- Review reliability and cost together: compare repeated runs with consistent task and success definitions, including failures and retries in the accounting you report.
These controls make failures easier to locate and recovery more deliberate; they do not make a non-deterministic agent deterministic. Their effectiveness has to be assessed against the outcomes and operating costs of the system’s actual workload.
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