TL;DR: Agentforce is only as good as the Salesforce org it runs on. Governor limit errors, permission failures, and unclean data don't just slow down your agents — they stop them in their tracks. Here are the five technical issues most likely to derail your AI deployment, and how to fix them before you go live.

The Hidden Problem with Agentforce Deployments

You've built the agent. Let's go.

You've configured the topics. Alright. Nice.

You've written instructions that would make a prompt engineer proud.... Then you deploy, and within hours, you're seeing errors in your debug logs, failed actions, and an AI that hallucinates because it can't access the data it needs.

Here's what nobody tells you about Agentforce: it inherits all your org's technical debt.

Every governor limit issue, every permission gap, every automation conflict that's been quietly lurking in your Salesforce instance? Agentforce will find it. Because unlike a human user who can work around a slow page or retry a failed action, an AI agent either succeeds or fails. There's no middle ground.

The good news: these problems are predictable. The same five errors show up in org after org. Fix them before deployment, and your Agentforce rollout goes smoothly. Ignore them, and you'll spend your first month firefighting instead of optimizing.

Error #1: Apex CPU Time Limit Exceeded

The Error: System.LimitException: Apex CPU time limit exceeded

Why Agentforce Makes It Worse:

Agentforce actions don't run in isolation. When your agent executes a Flow action to create a Case, that action triggers every automation on the Case object — triggers, Flows, validation rules, workflow rules. Each one consumes CPU time from the same 10-second budget.

In a typical org, a single user creating a Case might use 3-4 seconds of CPU time. No problem. But when Agentforce is handling 50 concurrent conversations, each creating Cases, those automations stack up fast. And because Agentforce actions often chain together (identify customer → look up order → create case → send confirmation), a single agent interaction can trigger dozens of downstream automations.

The Agentforce-Specific Risk:

The Atlas reasoning engine executes actions asynchronously and in sequence. If one action in the chain times out, the entire conversation context can be lost. The agent doesn't gracefully degrade — it fails, often with a vague error message that tells the end user nothing helpful.

How to Prevent It:

1. Audit your automation before deployment. For every object your agent will touch (typically Contact, Case, Account, Opportunity), map every trigger, Flow, and process that fires on create/update.
2. Consolidate automations. If you have three Flows firing on Case creation, combine them into one. One Flow = one execution context = predictable CPU usage.
3. Use before-save Flows where possible. They're significantly faster than after-save Flows because they don't require additional DML operations.
4. Test with realistic concurrency. Don't just test one agent conversation—simulate 20 concurrent users hitting the same automation paths.
5. Add circuit breakers. Use Limits.getCpuTime() in Apex to check remaining CPU budget before expensive operations, and fail gracefully rather than timing out.

Error #2: Too Many DML Statements: 151

The Error: System.LimitException: Too many DML statements: 151

Why Agentforce Makes It Worse:

Agentforce agents often need to touch multiple objects in a single interaction. A Service Agent handling a return request might need to:

• Look up the Contact
• Query the Order
• Create a Case
• Create a Return record
• Update the Order status
• Create a Task for follow-up
• Log an Activity

That's 7 objects. If your automations aren't bulkified — if each object update triggers additional DML operations — you can hit 150 statements before the agent finishes a single customer request.

The Agentforce-Specific Risk:

Flow-based agent actions are particularly vulnerable. If your Flow loops through records and updates each one individually (a common pattern for admins who haven't learned bulkification), you'll hit this limit quickly. And because Agentforce actions are often built by admins using Flow Builder rather than developers writing Apex, the bulkification patterns that developers take for granted are frequently missing.

How to Prevent It:

1. Audit Flow actions for DML inside loops. Open every Flow that your agent uses as an action. If you see an Update Records element inside a Loop element, refactor it.
2. Use collection variables. Instead of updating records one at a time, collect them in a collection variable and update them all at once after the loop.
3. Check your trigger chain depth. If creating a Case triggers an update to Account, which triggers an update to Contacts, which triggers... you get the idea. Map the full chain.
4. Consider async patterns. For complex multi-object updates, use Platform Events or Queueable Apex to break the work across multiple transactions.

Error #3: INSUFFICIENT_ACCESS_ON_CROSS_REFERENCE_ENTITY

The Error: INSUFFICIENT_ACCESS_ON_CROSS_REFERENCE_ENTITY: insufficient access rights on cross-reference id

Why Agentforce Makes It Worse:

Every Agentforce agent runs as a dedicated "Agent User"—a system user with its own profile and permission sets. This user needs access to every object, field, and record that any action might touch. Miss one permission, and you get this cryptic error.

The problem is that permission requirements are often hidden. Your agent action queries Contacts? The Agent User needs Read access to Contacts. The action creates a Case? Write access to Cases. The Case has a lookup to a custom Product object? Read access to that too. And if any of those objects use record-level sharing (OWD = Private), the Agent User needs sharing rules or explicit access.

The Agentforce-Specific Risk:

This error is particularly insidious because it often works in testing and fails in production. You test with your admin account (which has full access), everything works great, then you deploy and the Agent User can't see half the records it needs.

It's also context-dependent. If your agent action queries "the customer's most recent Order," and that Order was created by a user with a different role, the Agent User might not be able to see it—even if it can see other Orders just fine.

How to Prevent It:

1. Create a dedicated permission set for your Agent User. Don't rely on profile permissions alone. Document every object, field, and record type the agent needs.
2. Test as the Agent User. Log in as (or impersonate) the Agent User and manually perform every action your agent will take. If you can't do it manually, the agent can't do it programmatically.
3. Check sharing rules. If your org uses Private OWD on any objects the agent touches, ensure the Agent User has appropriate sharing access—either through sharing rules, role hierarchy, or explicit sharing.
4. Audit related objects. If your action touches Contacts, check that the Agent User can also access Accounts (via the lookup), any custom objects related to Contacts, and any objects referenced in formula fields.
5. Enable "View All" cautiously. It's tempting to give the Agent User "View All Data" to avoid permission issues. Don't. This creates security risks and masks real permission problems that will bite you later.

Error #4: FIELD_CUSTOM_VALIDATION_EXCEPTION

The Error: FIELD_CUSTOM_VALIDATION_EXCEPTION: [Your validation rule error message]

Why Agentforce Makes It Worse:

Validation rules are designed to enforce data quality by blocking saves that don't meet business criteria. Great for humans who can read the error message and fix their input. Terrible for AI agents that have no idea why their action failed.

When an Agentforce action tries to create or update a record and hits a validation rule, it fails silently from the user's perspective. The agent might retry with the same bad data, or give up entirely. Either way, the customer sees "Something went wrong" instead of a helpful response.

The Agentforce-Specific Risk:

Validation rules often assume human context that agents don't have. "Phone number is required when Source = Web" makes sense when a human is filling out a form. But if your agent is creating a Lead from a chat conversation, it might not have collected a phone number—and now it can't save the record.

The other risk is validation rules on related objects. Your agent creates a Case successfully, but the Case triggers a Flow that updates the Account, and the Account has a validation rule that fails. The entire transaction rolls back, including the Case that was fine.

How to Prevent It:

1. Audit validation rules for agent-created records. For every object your agent creates or updates, review every validation rule. Ask: "Could an agent trigger this rule? If so, what happens?"
2. Make required fields actually required. If a field must be populated for business reasons, make it required at the field level rather than enforcing it via validation rule. Agents handle required fields better than validation rules.
3. Add agent-aware conditions to validation rules. You can exclude the Agent User from certain validation rules using $User.Id != 'your_agent_user_id' or by checking a custom permission.
4. Design prompts to collect required data. If your validation rule requires a phone number, make sure your agent's topic instructions tell it to ask for one.
5. Handle validation errors gracefully in Flows. Use fault paths to catch validation errors and respond with helpful messages rather than generic failures.

Error #5: The Silent Killer — Dirty Data

The Error: No error at all. Just wrong answers, hallucinations, and customer frustration.

Why Agentforce Makes It Worse:

Governor limits and permission errors are at least visible — you get an error message, you can debug. Dirty data is invisible until a customer complains.

Agentforce agents pull information from your CRM to answer questions and take actions. If that data is wrong, the agent confidently gives wrong answers. Duplicate Contacts mean the agent might pull the wrong customer's order history. Stale Account data means the agent quotes the wrong pricing. Missing fields mean the agent has to guess — and AI guesses are called hallucinations.

The Agentforce-Specific Risk:

This is actually three problems:

1. Duplicate records. If your agent queries "Contacts WHERE Email = 'john@example.com (mailto:john@example.com)'" and gets three results, which one does it use? Often the first one returned by the query—which might be the oldest, least accurate record.
2. Incomplete records. Your Knowledge articles have the right content but missing metadata? The agent's RAG (Retrieval Augmented Generation) can't find them. Your Accounts are missing Industry values? The agent can't filter or personalize based on industry.
3. Inconsistent data. "United States" in one field, "USA" in another, "U.S." in a third. Humans handle this fine. AI agents treat them as three different values.

How to Prevent It:

1. Deduplicate before deployment. Run duplicate reports on every object the agent will query. Use Salesforce's Duplicate Management or third-party tools to merge records and set up duplicate rules to prevent new ones.
2. Identify critical fields. For each agent action, identify the fields that must be populated and accurate. Create reports showing records with missing critical data, and fix them.
3. Standardize picklist values. Audit your picklists for inconsistencies. "Prospect" vs "Prospective" vs "Prospecting" should be one value, not three.
4. Clean up your Knowledge base. If your agent uses "Answer Questions with Knowledge," audit your Knowledge articles. Are they current? Are they tagged correctly? Do they have the right data categories?
5. Set up data governance for the long term. Validation rules, required fields, and Flows can enforce data quality at entry. Scheduled reports can flag data that drifts out of compliance.

The Pre-Deployment Checklist

Before you activate your Agentforce agent, run through this list:

Governor Limits

• Mapped all automations on objects the agent touches
• Consolidated multiple Flows/triggers into single executions
• Tested with realistic concurrency (20+ simultaneous users)
• Verified no DML inside loops in Flow actions
• Confirmed CPU usage stays under 7 seconds with buffer

Permissions

• Created dedicated Agent User permission set
• Granted access to all required objects and fields
• Tested every action logged in as Agent User
• Reviewed sharing rules for Private OWD objects
• Verified access to related/lookup objects

Validation Rules

• Audited validation rules on all agent-touched objects
• Added agent-aware conditions where appropriate
• Designed prompts to collect required field data
• Implemented fault paths for graceful error handling

Data Quality

• Deduplicated Contact, Account, Lead records
• Populated critical fields required by agent actions
• Standardized picklist values and formatting
• Cleaned and categorized Knowledge articles
• Set up ongoing data governance automation

The Pattern: Your Org's Health = Your Agent's Performance

Notice what all five of these errors have in common?

None of them are directly Agentforce problems!

They're more like... Salesforce problems that Agentforce exposes.

The CPU limits were always there — but humans are slower than agents, so they rarely hit them. The permission gaps were always there (but admins have full access, so they never noticed). The dirty data was always there —but humans (with enough time) could work around it.

Agentforce is a forcing function. It makes you fix the technical debt you've been ignoring. The orgs that struggle with Agentforce are the orgs that have been accumulating automation sprawl, permission creep, and data quality issues for years. The orgs that succeed are the ones that use Agentforce as the reason to finally clean house.

That's actually good news. Because once you fix these issues for Agentforce, you've fixed them for everything. Your Flows run faster. Your users have cleaner data. Your integrations are more reliable. Agentforce doesn't just run better—your whole org does.

Using Sweep? Here's How to Get Agentforce-Ready Faster

If you're planning an Agentforce deployment, Sweep's Agentic Assessment can identify these issues before they become production problems.

Visualize Your Automation Chain

Before you deploy an agent, you need to know what it will trigger. Sweep's Visualizer shows every automation on every object — triggers, Flows, validation rules, and their dependencies. If creating a Case triggers 12 downstream automations, you'll see that immediately.

Find the Governor Limit Risks

Sweep identifies automation patterns that are likely to cause CPU and DML limit issues: multiple Flows on the same object, DML inside loops, circular dependencies. You'll know exactly where to consolidate before you hit limits in production.

Audit Your Metadata Health

Agentforce depends on clean, well-structured metadata. Sweep analyzes your fields, objects, and relationships to identify inconsistencies that could confuse an AI agent — duplicate fields, unclear naming conventions, missing descriptions.

Plan Your Remediation

The Agentic Assessment doesn't just identify problems — it prioritizes them. You'll know which issues will break your agent on day one versus which can wait for v2.

Book some time to learn about our Agentic Assessment here.