Agentforce long-horizon agents keep one goal alive for weeks with memory, durable execution and steering. See how Hunter works and how to prepare your org.
Your Agent Forgets Everything When the Chat Closes. Long-Horizon Agents Don’t.
Salesforce’s new long-horizon runtime lets an Agentforce agent chase one goal for days, weeks or months, carrying memory between sessions and taking direction in plain language. Here’s how it works, what Hunter does with it, and what to settle in your org before Hunter reaches GA in November.
Most Agentforce agents work in sessions: a request comes in, the agent acts, and the context disappears when the chat ends. The long-horizon runtime, launched with Hunter at Dreamforce 2026, keeps one goal alive for weeks by giving the agent memory across sessions, a plan that survives interruptions, and direction you can change in plain language. Teams that get the most from it will settle approval rules, action design and data hygiene now, before Hunter reaches GA in November.
In This Article
It’s week ten of the quarter. An account executive has six deals that went quiet in August, so she asks her Agentforce agent for help. The agent does exactly what it was built to do: it drafts six follow-up emails, logs the activities and closes the session. Ten days later two prospects have replied, one email has bounced, and the agent has no idea any of it happened.
Nothing broke there. That’s simply how nearly every enterprise agent has worked for the past two years, on any platform. A request arrives, the agent reasons, acts and responds, and the context goes away with the chat window.
For a lot of work, that model is perfect. Resetting a password or summarizing a case is a sprint, and sprints should end quickly. The work that moves revenue is usually shaped differently. Rescuing a stalled deal is a campaign. So is rebuilding a field service schedule after a technician calls out sick on launch day.
Salesforce’s answer is the long-horizon runtime, a new layer under Agentforce built to keep one goal alive for days, weeks or even months. Hunter, the outbound sales agent, is the first agent to run on it. It’s in pilot today, with general availability targeted for November 2026.
Salesforce delivered 3.2 billion Agentic Work Units across Agentforce and Slack in Q2 alone, so agents are already doing real work at volume. The long-horizon runtime opens up the next category: jobs that used to need a person to remember what happened last Tuesday.
For architects, the more useful question is what changes in your org once an agent stops forgetting.
Salesforce describes the runtime with three attributes. Each one fixes a specific way session agents fall over on long jobs, which is why they’re worth taking one at a time.
Carries context and progress from one session to the next. The agent knows who replied, which email bounced and what you told it last week, so you never re-explain the deal.
Keeps the plan running over time and lets the agent course-correct when a step fails. A bounced email adds a new step to the plan instead of ending it.
Lets you redirect a running agent just by talking to it. Ask for a more formal tone with VPs and the rest of the plan adjusts.
If you’ve worked with workflow engines, durable execution will feel familiar. Long-running orchestrations survive restarts for the same reason: the state of the plan lives outside any single run, so a failure turns into a resume point. The new part is pairing that durability with an LLM planner that can rewrite the remaining steps when the situation changes.
These agents also show up wherever people already work. Salesforce lists Agentforce Coworker, Slack and third-party apps as surfaces, which means a seller can check on a two-week plan from the same place they check on everything else.
- Context lives in the conversation and ends with it
- A failed step usually comes back to the user as an error
- Each new request starts from a blank plan
- Best for: questions, lookups and single actions
- Memory carries context and progress between sessions
- Durable execution resumes or reroutes after a failed step
- You adjust the plan in conversation while it runs
- Best for: deal rescues, campaigns and multi-week jobs
Salesforce’s own walkthrough uses a scenario every sales leader knows: an account executive close to quarter-end with a handful of at-risk deals. Here’s the flow, with the design detail that matters at each stage.
Something like “re-engage my at-risk deals.” Hunter looks through open opportunities and pulls engagement signals from Data 360 and Slack before it plans anything.
“Re-engage my at-risk deals” becomes something like “re-engage $340K of at-risk pipeline by quarter-end.” A measurable target lets the agent, and you, tell whether the plan is working.
Every task gets timing and a rule for when Hunter checks in for approval. The seller reviews it in a side panel, adjusts anything that looks off, then switches it on.
Hunter asks for email access so it can send on the seller’s behalf and match how they write. Steering shows up here too, so the seller can ask for a formal tone with VPs and a lighter one over WhatsApp.
The recipient is out on leave. Hunter finds an alternate contact and drafts a new message, but since that departs from the approved plan, it asks the seller before sending. Then it suggests how to handle future out-of-office replies.
Step five is the one I keep coming back to. A session agent hitting that bounce would retry, fail quietly or hand the error back to the user. Hunter treats it as new information, proposes a change and waits for a person to agree. That’s the behavior you’d want from a sharp junior rep, and it’s what makes a weeks-long agent safe to run.
Salesforce reports that 60% of Perk’s sales pipeline is now built by Hunter. The runtime is still in pilot, and it’s already building pipeline for a real sales team.
This is where architects earn their keep. A session agent’s mistakes stay inside one conversation. A long-horizon agent’s decisions compound over weeks, so a few choices that used to be optional now carry real weight.
What Decision 3 Looks Like in Apex
The invocable action below creates a follow-up task. The agent passes a step key that stays the same across resumes (the opportunity ID plus the plan step name works well), and the action returns the existing task instead of inserting a duplicate.
APEXpublic with sharing class CreateFollowUpTask { public class Request { @InvocableVariable(required=true) public Id opportunityId; @InvocableVariable(required=true) public String stepKey; // stable across resumes @InvocableVariable public String subject; } @InvocableMethod(label='Create Follow-Up Task (Idempotent)') public static List<Id> run(List<Request> requests) { Set<String> keys = new Set<String>(); for (Request r : requests) { keys.add(r.stepKey); } // A resumed step finds the task it already created Map<String, Id> byKey = new Map<String, Id>(); for (Task t : [SELECT Id, Agent_Step_Key__c FROM Task WHERE Agent_Step_Key__c IN :keys]) { byKey.put(t.Agent_Step_Key__c, t.Id); } Map<String, Task> toInsert = new Map<String, Task>(); for (Request r : requests) { if (!byKey.containsKey(r.stepKey) && !toInsert.containsKey(r.stepKey)) { toInsert.put(r.stepKey, new Task(WhatId = r.opportunityId, Subject = r.subject, Agent_Step_Key__c = r.stepKey, ActivityDate = Date.today().addDays(2))); } } if (!toInsert.isEmpty()) { insert toInsert.values(); } for (Task t : toInsert.values()) { byKey.put(t.Agent_Step_Key__c, t.Id); } List<Id> results = new List<Id>(); for (Request r : requests) { results.add(byKey.get(r.stepKey)); } return results; } }
Agent_Step_Key__c is a plain text field on Activity. It’s twenty minutes of work, and it removes a whole class of “why did the customer get two emails?” tickets before they exist.
Where to Draw the Autonomy Line
| Agent Action | Suggested Mode | Why |
|---|---|---|
| Log an activity or update Next Step | Autonomous | Low risk and easy to reverse |
| Follow up with a contact already in the approved plan | Autonomous | The seller signed off when activating the plan |
| Email a new contact the agent discovered | Seller approval | Departs from the approved plan |
| Change opportunity amount, stage or close date | Seller approval | Feeds the forecast and commissions |
| Offer a discount or other concession | Manager approval | It’s a commercial commitment |
The runtime handles memory, persistence and steering. Your business rules are still yours to write. None of the work below is new to anyone who has shipped a serious integration, but each item matters more when an agent keeps working for weeks on your behalf.
Hunter builds its plan from your open opportunities and engagement signals. Stale close dates and missing contact roles point a good plan at the wrong deals.
With too few check-ins, sellers get nervous. With too many, the agent turns into another inbox to clear. Start tight and relax guardrails as the results earn it.
Every goal needs an end state: target hit, deadline passed or deal closed lost. Write these down with RevOps before anyone clicks activate.
Agent Health Monitoring watches 16+ built-in reliability metrics, and Custom Scorers grade sessions against logic you define. Add a simple goal-progress view so each long-running goal is visible at a glance.
Sellers should know what the agent will do in their name and how to steer it. A short enablement session up front saves weeks of confused overrides later.
Long-lived agent memory is customer data like any other. Before you scale past the pilot, agree on retention and access with your governance team, and check both against your consent records.
Pilot Readiness Checklist
Hunter is the first agent on the runtime, not the last. Salesforce plans to move more agents onto it and eventually let customers build their own long-horizon agents with Agent Script.
Salesforce’s field service example shows where this heads. Picture a CPG dispatcher on product launch day when the rep covering a major account calls in sick. Finding a replacement by hand means juggling who’s on shift, who’s nearby and who knows the store. A long-horizon agent tracking that goal would surface a recommended swap, and the dispatcher’s only job is yes or no.
Pair the runtime with Agent Optimizer, which reaches GA in October. It works through large volumes of production sessions, groups the failures that repeat, and puts the costliest ones at the top of the list. For agents that work across weeks, that feedback loop is how a pilot grows into a program.
The Agentforce runtime that lets an agent pursue one goal across days, weeks or months.
Keeping a plan’s state outside any single session so it can resume and course-correct after interruptions.
Changing an active agent’s plan or configuration through conversation, without rebuilding the agent.
A prebuilt Agentforce agent for a specific role, like Hunter for outbound sales or Casey for customer service.
Salesforce’s unit for counting the work agents complete across Agentforce and Slack.
An action that produces the same result whether it runs once or several times.
Salesforce’s product team shows the plan preview, approval checkpoints and the out-of-office recovery step by step.
Most Agentforce roadmaps still list use cases as questions an agent can answer. Starting now, a better list is goals an agent can own. If you had an agent that remembered everything for six weeks, which goal would you hand it first?