---
type: "post"
id: "how-to-build-proactive-always-on-agents-with-9bd942"
slug: "how-to-build-proactive-always-on-agents-with-9bd942"
title: "How to Build Proactive, Always-On Agents with the Schedules SDK in Managed Deep Agents"
description: "LangChain has described a Schedules SDK for Managed Deep Agents, allowing scheduled agent runs to be defined in an agent configuration or created at runtime through tools."
retrieval_nugget: "LangChain has described a Schedules SDK for Managed Deep Agents, allowing scheduled agent runs to be defined in an agent configuration or created at runtime through tools. For builders, the important shift is practical: persistent behavior can be expressed as a governed wake-up loop tied to a user’s existing thread and memory context."
published_at: "2026-10-28"
updated_at: "2026-10-28"
record_date: "2026-10-28"
date_kind: "published_at"
topics: ["agent-loops","automation"]
entities: ["LangChain","Managed Deep Agents","LangSmith","Schedules SDK"]
related_patterns: ["goal-scoped-loops-replace-manual-continuation","harness-architecture-outlives-model-choice","company-memory-needs-write-loops"]
novelty_label: "new_fact"
what_changed: "The article says more than 70% of Managed Deep Agents runs on LangSmith are scheduled rather than user-initiated."
prior_refs: ["memory:7ba0e82a-ea47-8067-aee3-f451c6db814c","memory:b11ef86e-8dad-8821-ab1a-fcfb37b078ea"]
source_url: "https://www.langchain.com/blog/proactive-agents"
source_urls: ["https://www.langchain.com/blog/proactive-agents"]
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stable_id: "post:how-to-build-proactive-always-on-agents-with-9bd942"
status: "published"
visuals: []
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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---

# How to Build Proactive, Always-On Agents with the Schedules SDK in Managed Deep Agents

## Retrieval answer

LangChain has described a Schedules SDK for Managed Deep Agents, allowing scheduled agent runs to be defined in an agent configuration or created at runtime through tools. For builders, the important shift is practical: persistent behavior can be expressed as a governed wake-up loop tied to a user’s existing thread and memory context.

## Summary
LangChain has described a Schedules SDK for Managed Deep Agents, allowing scheduled agent runs to be defined in an agent configuration or created at runtime through tools. For builders, the important shift is practical: persistent behavior can be expressed as a governed wake-up loop tied to a user’s existing thread and memory context.

## What happened
In an October 28, 2026 post, LangChain describes how to build “proactive” or always-on agents with schedules in Managed Deep Agents, which are hosted through LangSmith Deployments. The core mechanism is familiar infrastructure: a cron-style scheduler wakes an agent on a defined cadence and supplies a prompt. LangChain presents this as the basis for agents that act without a user typing a fresh request each time.

The post maps several behaviors onto scheduled runs. A background run can condense prior conversations into longer-term memory, a process it calls “dreaming.” A recurring run can examine whether there is a useful next action toward a general or explicit goal and send a proactive message. Other cited uses include triaging event streams such as Slack or email, delivering a requested reminder, and having an orchestration agent check on a fleet or asynchronous subtask after sleeping. LangChain says that more than 70% of Managed Deep Agent runs on LangSmith come from scheduled runs rather than a human entering a message.

The company says the implementation needs two components. First is a cron server that lets developers attach prompts, schedules, required skills, and permissions to jobs, while adding jitter when many jobs would start at the same moment. Second is an agent-facing but governed interface for creating and changing reminders. The emphasis on governance matters because the agent itself may be permitted to alter its future work schedule.

Managed Deep Agents support both static and dynamic schedules. A static daily sales readout can be placed in a `schedules/` file with a cron expression, timezone, and prompt. For dynamic scheduling, developers wrap the Schedules SDK in tools. LangChain’s example exposes `set_schedule`, `list_schedules`, and `delete_schedule`; creation passes a user owner, cron expression, and prompt to the runtime. A smaller `check_in_later` tool creates a one-off run at a calculated future UTC time. The post says a scheduled run uses the original prompt and the thread and memory associated with the principal who created it.

## What the archive adds
This is a new implementation detail on a known persistent-agent story. The earlier Field Note on [Claude Managed Agents with AG-UI compatibility](https://newruntime.com/posts/claude-managed-agents-with-ag-ui-compatibility-turns-agent-state-into-a-fron/) treated agent state as a frontend contract. The [Memory Tax study](https://newruntime.com/posts/memory-tax-paper-finds-many-dedicated-agent-memory-f40f56/) adds an important qualification: persistent memory mechanisms do not automatically outperform straightforward retrieval over raw history.

## Trend signal
This supports [Goal-scoped loops replace manual continuation](https://newruntime.com/patterns/goal-scoped-loops-replace-manual-continuation/): schedules give an agent an external wake-up mechanism through which it can revisit an explicit task. It also supports [Harness architecture outlives model choice](https://newruntime.com/patterns/harness-architecture-outlives-model-choice/). The useful product surface here is scheduling, ownership, tools, permissions, memory, and deployment behavior rather than a particular model.

## Why now
LangChain frames proactive agents as an emerging design pattern and points to several named product launches as evidence of attention around ambient behavior. Its own usage figure supplies the more concrete timing signal: scheduled runs already account for over 70% of Managed Deep Agent runs on LangSmith, making scheduling a deployed operating pattern rather than only a conceptual feature.

## Connections to other events
The archive’s earlier Deep Agents material described the harness in terms of orchestration, memory, tools, sandboxes, skills, and configuration. Schedules add a temporal control plane to that same stack: an agent can now be configured not only with what it may do, but also with when it returns to work. The related Memory Tax result remains a counterweight to claims that periodic memory consolidation necessarily improves outcomes.

## Why it matters
Many agent prototypes are reactive because a chat turn is their only execution boundary. A scheduler changes that boundary. Teams can define inspection loops around incoming work, goal progress, reminders, and asynchronous jobs without requiring a person to reopen the conversation. That can make a workflow more useful, but it also turns cadence and authorization into production design choices.

The practical question is not whether an agent can run every hour. It is what it is allowed to inspect, what state it can update, who owns that state, how it decides an alert is worth sending, and how operators can remove or amend future runs. LangChain’s examples make those interfaces explicit enough to prototype. They do not remove the need to evaluate noise, errors, unwanted actions, or the cost of recurring execution.

## What happens next
- **Teams will expose narrower, task-specific scheduling tools rather than a generic unrestricted scheduler.** (3 months, 70%). Watch for: wrappers such as reminder or check-in tools with constrained inputs.
- **Scheduled-run evaluation will become a distinct operational concern.** (6 months, 60%). Watch for: metrics on alert relevance, duplicate actions, and schedule-created run volume.

## Limits and uncertainty
The source is a LangChain product post, not an independent evaluation of Managed Deep Agents or a reliability study. The reported share of scheduled runs is not accompanied by customer counts, task-completion rates, error rates, pricing, execution limits, or a definition of which run types are included. The post shows APIs and implementation sketches but does not establish how permissions are enforced across tools, how missed schedules are handled, or whether periodic memory consolidation improves a particular agent’s quality. It also does not compare this scheduler with alternatives.

## Skill to build now
Take one existing reactive agent and write a narrowly bounded scheduled job for it this week. Define the owner, one source it may inspect, a cadence, a single observable output, and a deletion path. For example, run a weekday morning KPI inspection that returns only a short exception report. Add a test set of normal and noisy inputs before enabling notifications. Treat the schedule-creation interface as a permissioned product feature, not as an unrestricted natural-language command.

## Reader takeaway
Schedules make persistent agents concrete: a future run is a prompt, a cadence, an owner, and state carried into the runtime. That is enough to build useful follow-ups, monitoring, and orchestration loops, but only if teams specify the permissions, alert threshold, and review path around each loop.

## Open question
Can teams give agents enough freedom to create useful recurring work without allowing schedule proliferation, noisy notifications, or unauthorized access to a user’s persistent context?

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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