Agentforce: Govern Content for AI Consumption

AI agent quality depends directly on the quality of the content it retrieves. Retrieval-augmented generation (RAG) systems retrieve content in fragments and rely on explicit signals to distinguish sources, audiences, and contexts. To the retrieval system, a knowledge article is a record: indexed, filtered, and ranked just like structured data. Content that wasn't prepared for this retrieval model produces inconsistent, incomplete, or incorrectly targeted responses.

What Happens Without Content Governance

Consider a growing e-commerce company with a single "Returns and Refunds" article written to serve everyone: customers asking about timelines, support reps handling exceptions, and finance tracking chargebacks. It covers the 30-day return policy, screenshots of the refund tool with internal notes about fraud flags and manager approval thresholds, and chargeback procedures that reference an outdated 14-day return window alongside the current 30-day policy.

When a customer asks about returning a damaged item, the agent retrieves fragments from this one article. Without clear signals about audience, structure, or context, it might expose internal approval thresholds to a customer, skip critical workflow steps when responding to a support rep, combine the old 14-day return window with the current 30-day policy, or miss information embedded in screenshots that lack descriptive alt-text.

The problem isn't missing content. It's content that wasn't prepared for how RAG retrieval works. The SODA framework (Specific, Organized, Detailed, Accurate) can turn an existing knowledge base into an AI-ready asset. These principles align with Knowledge-Centered Service (KCS) best practices and apply to any knowledge base feeding an Agentforce agent. To learn more about KCS, visit the Consortium for Service Innovation.

Be Specific

AI agents perform best when each article focuses on a single, clearly defined topic for a single audience. Articles that combine multiple topics or audiences dilute embeddings and increase the risk of the model combining mismatched ideas.Split articles by audience. The "Returns and Refunds" article above should become three distinct records:

A customer-facing "How to Return an Item" article covering steps and timelines
A support guide with exception workflows and approval thresholds
A finance document for chargeback procedures and accounting codes

Use consistent terminology across all three. If one article uses "refund" and another uses "credit," the retrieval system may treat them as different concepts. Define abbreviations and mark outdated terms as deprecated.Writing for a specific audience improves relevance, but it doesn't enforce separation. Preventing an internal article from surfacing to a customer-facing agent takes two mechanisms:Pre-retrieval separation: Create dedicated fields or articles for each audience. Explicitly indicating a target audience minimizes cross-audience ambiguity.Run-time filtering: Use access-tier metadata so agents retrieve only the content appropriate for the user or use case. In Salesforce Knowledge, access is controlled through Object and Field Level Security (Knowledge User permission, Knowledge__ka and Knowledge__kav object access), data category filters, and Custom Metadata Type Permissions for custom access-tier fields. Retriever prefilters reinforce this at the retrieval layer.With separation in place, a support rep asking about manager approvals gets the internal guide, and a customer never sees it.

Scope decisions also affect retrieval at the chunk level. RAG systems retrieve short passages, not whole articles, so a mixed-audience article produces mixed-audience fragments. Splitting by audience keeps each chunk within its intended scope. Where those chunk boundaries fall depends on the article's internal structure.

Be Organized

AI retrieval doesn't interpret visual formatting cues reliably. Bold text, indentation, and whitespace don't signal semantic structure. The retrieval system reads heading tags, dedicated fields, and metadata. These signals determine how ideas relate and where chunk boundaries fall.

Use actual heading tags: H1 for the article title, H2 for major sections like "Standard Refund Process" and "Exceptions and Approvals," and H3 for specific scenarios. Spread content across dedicated fields like Question, Resolution, and Exceptions instead of a single text block. Add metadata tags for topic, version, audience, and access tier.

How Structure Affects Chunking

A chunk is a short passage the search index creates before vectorization, and a good chunk answers a question on its own. Heading tags, field boundaries, and short sections tell the system what belongs together. Without clear structure, common problems occur:

A cause gets split from its resolution
A condition gets separated from the step it governs
A passage opens mid-explanation with no identifying context

Use Intelligent Context to preview how your content is being chunked before publishing. Fragmented or orphaned passages are a signal to tighten structure in the source article.

Be Detailed

AI agents need sufficient context to provide accurate responses. This isn't about longer content; it's about content complete enough to be useful, with the conditions, exceptions, and version applicability spelled out and the real-world pitfalls documented. When context is thin, language models fill the gaps with plausible-sounding but potentially incorrect completions (hallucinations).

"Refunds take 5 to 7 business days" is incomplete. From when? After the package is received, inspected, or processed? The complete version specifies the full timeline: inspection takes 2 business days after receipt, processing takes 5 to 7 business days, and credit card refunds may take another 3 to 5 days depending on the bank. Add version applicability at the top: "This policy applies to orders placed after March 1, 2024. Older orders have a 14-day return window instead of 30."

The same applies to internal procedures. Support teams know that customers often expect refunds immediately after dropping off a package, that expired credit cards cause silent failures, and that high-value refunds trigger fraud flags even for legitimate returns. Document these pitfalls explicitly instead of assuming the agent will infer them.

For screenshots and diagrams, add alt-text that describes what's on screen: objects, visible text, layout, colors, relationships, and overall context. Agentforce Data Library detects content modes and applies the most appropriate parser for images and multimodal content, but descriptive alt-text still improves retrieval accuracy, searchability, and consistency.

Be Accurate

AI agents treat your knowledge base as authoritative. Outdated or inconsistent content produces confidently wrong answers.

Review content with subject matter experts before publishing. When policies change, update related articles immediately and remove near-duplicates. Conflicting or redundant articles create content noise that degrades response quality. Version content explicitly: state which dates, product versions, or conditions apply, and mark outdated articles so agents don't present old guidance as current.

Test before and after deployment. Use Retriever Playground and Agentforce Testing Center to verify accurate retrieval. Both tools can generate test cases from connected knowledge sources. After deployment, monitor real sessions with Agent Analytics, Knowledge/RAG Quality Data and Metrics, and Agent Optimization. Patterns in agent behavior, user corrections, or escalations reveal gaps in the knowledge base.

When escalation rates spike after a policy change, version markers and analytics make it clear which articles still carry old guidance.

Use Data to Identify Where to Start

Before revising your entire knowledge base, use retrieval quality metrics to identify where to focus. Three metrics give you a diagnostic picture:

Metric	What it measures	Low score means
Context Precision	Whether the agent retrieved the right content	Content quality problem: articles are too broad, terminology is inconsistent, or audience segmentation is weak
Faithfulness	Whether the agent used what it retrieved correctly	Prompt template or model configuration issue; not a content problem
Answer Relevance	Whether the response actually answered the question	Coverage gap: the content doesn't exist or isn't sufficient to fully answer the query

Cross-reference these with Agent Analytics: which topics have the lowest resolution rates, where do conversations escalate, where do users correct or abandon the agent? You can inspect individual sessions to see exactly what went wrong. That intersection points to the highest-priority content to address first.

Content Governance Maturity

Treat content governance as a maturity model with a structured adoption path. Start with your highest-priority content, apply the framework, and expand from there.

Stage 1: Isolate and Tag

Goal: Give the retrieval system basic signals to distinguish content types.

These changes don't require rewriting content:

Review high-priority articles to confirm each addresses a single user intent. Flag articles that mix multiple audiences for restructuring.
Replace visual formatting with semantic structure. Add heading tags (H1, H2) instead of bold text or manual spacing.
Add foundational metadata: topic, audience, version.

Stage 2: Structure and Segment

Goal: Enable the retrieval system to make reliable audience-based decisions.

Split multi-audience articles into separate records (customer-facing, support agent, finance).
Distribute content across dedicated fields (Synopsis, Cause, Resolution, Exceptions) instead of single text blocks.
Add access-tier and version-control metadata to enable run-time filtering.

Stage 3: Deepen Context and Modality

Goal: Give the agent enough context to handle follow-up questions, edge cases, and version-specific guidance.

You may need input from support, finance, or policy teams to get the details right.

Define an audit and review process, such as a publishing checklist or validation rules that prevent incomplete articles from going live.
Add version constraints: "Applies to orders placed after March 1, 2024."
Document common failure points, edge cases, and exception handling.
Add descriptive alt-text for screenshots and diagrams.

Stage 4: Operationalize

Goal: Make content governance part of regular platform maintenance.

Test changes in Retriever Playground and Agentforce Testing Center before publishing.
Add a "Last reviewed" date field and flag content older than six months for audit.
Build SME approval steps into publishing workflows. Add a required review step or "SME Verified" checkbox before articles can go live.
Tie content updates to the policy change process. When a policy team updates a return window, updating the knowledge base should be part of the same rollout checklist.
Schedule a regular review of Agent Analytics and RAG Quality Metrics to catch gaps before they accumulate.

For many teams, Stage 4 is when content governance stops being a project and becomes part of platform health, alongside monitoring automation, reviewing security posture, and checking data quality. Applied consistently, these principles transform your existing content into a reliable foundation for every agent response.