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Three Context Layers Every Agentforce Implementation Needs (And How to Build Them)
You won the Agentforce deal. Now you need to deliver.
Your client expects their agent to answer customer questions, route service cases, and automate quote generation. They signed the contract. They’re ready to see results.
Then you open their org. Twelve years of technical debt. Customer data spread across Salesforce, NetSuite, and a custom fulfillment system that nobody fully understands. Product codes that don’t match between systems.
Salesforce spent $8 billion on Informatica this week to solve this problem at the platform level. Their message is clear: AI agents need unified enterprise context to function. But owning Data 360, Informatica, and MuleSoft doesn’t automatically integrate them in your client’s org. That orchestration work falls to you.
We’ve deployed Agentforce across many enterprise implementations, and we’ve put together this guide to cover the three context layers we build for every deployment. Bonus: We’ve also included the mistakes that kill projects before they reach production.
If you’re facing your first Agentforce implementation or trying to salvage one that’s struggling, here’s what actually works.
Why Most Agentforce Implementations Fail
The data exists, but it doesn’t speak the same language. When your agent queries fragmented systems, it guesses. Salesforce’s answer is Data 360, Informatica, and MuleSoft working together.
The problem is that these platforms don’t automatically integrate themselves:
- Someone needs to map how your client’s ERP product codes translate to Salesforce SKUs.
- Someone needs to build the event streams that tell agents when inventory actually changes.
- Someone needs to establish which system holds the truth when customer addresses don’t match.
That’s orchestration. You’re connecting data sources, defining business rules, and building the semantic layer that lets agents understand what definitions actually mean in your client’s specific context. Salesforce gives you the platforms.
Here’s how to build context architecture that survives production.
Layer 1: Unified Business Objects
You need to map your client’s core entities (customers, products, orders, cases) across every system the agent will touch.
Semantic mismatches show up immediately - one system calls it “active customer,” another calls it “current account,” and a third has three different status fields that all mean slightly different things. The agent needs canonical definitions that work consistently across all systems.
The work is schema mapping and stakeholder alignment. Their ops team needs to agree on what these terms actually mean in their business, then you build the transformation logic that enforces those definitions.
This usually takes a few weeks of data modeling meetings and configuration work, assuming you can get everyone in the same room and aligned on the definitions.
We use Exo to flag schema mismatches during development. It catches when field mappings break or definitions start to drift.
Exo is our AI agent platform that acts as a business analyst and QA layer for Salesforce implementations - you can read more about how ExoHelp works to understand the full capabilities.
Even with Exo, the human work of establishing what “active” or “available” actually means in their business context can’t be automated. Someone has to make those calls.
What you’re building:
- Master definitions for each core entity
- Transformation logic between systems
- Validation that catches mismatches at record creation
- Documentation for the inevitable next integration
Plan for quarterly reviews because systems change, fields get added, and definitions drift over time. Better to catch it during a review than when something breaks in production.
Layer 2: Real-Time Operational Signals
Unified definitions fix your semantic problems, but you still need current data. An agent answering questions based on yesterday’s inventory gives wrong answers today, and users will notice immediately.
You need event streams that update the agent’s context as operations actually happen. When inventory moves, when a booking changes, when a payment fails.
Start by identifying which signals directly impact the agent’s decisions. For recruitment clients, candidate stage transitions matter.
For hospitality, it’s room availability and booking status. Don’t try to make everything real-time because you’ll burn weeks on signals that don’t actually affect agent behavior.
Set up event-driven updates that publish to wherever the agent queries from. When critical state changes, those updates need to flow through immediately. Core event streams take several weeks to build, longer if you’re working with source systems that have poor API coverage or require custom integration work.
Some legacy systems just won’t cooperate with real-time integration. When that happens, build fallback patterns and document where the agent’s context might lag behind reality. Your client needs to know these limitations exist.
Layer 3: Governance and Data Quality
Data quality problems compound fast in AI systems. Humans can look at mismatched addresses and figure out what’s actually correct. Agents can’t –– they see two different values and either pick one arbitrarily or refuse to act. Both outcomes break trust with users.
You need systems that catch data quality issues before they reach the agent and correct them automatically when possible. Build validation rules that fire at the point of entry. Set up duplicate detection that flags potential issues for human review.
Create dashboards that show where data quality is actively degrading so you can address problems before they multiply.
Exo monitors this continuously. It scans for common patterns like address issues, duplicate records, and invalid formats, then either auto-corrects or routes to ops for review. This approach catches problems faster than scheduled batch jobs and reduces the manual monitoring work your team has to do.
Focus on the quality issues that actually break agent functionality. You’re not going to eliminate every data quality problem, and trying to will consume unlimited time. Good enough data is achievable and gets you to production. Perfect data isn’t realistic.
One critical area many teams overlook is systematic batch testing for Agentforce prompts. Without it, you won’t catch regressions until users do.
This is ongoing work, not a one-time cleanup project. Data quality degrades constantly as people enter records, systems sync, and integrations run. You’re building maintenance processes that need to keep running.
The Orchestration Challenge
These three layers need to work together, not just exist independently. Unified business objects mean nothing if your real-time signals use different definitions. Real-time updates are useless if data quality is poor. Governance without semantic understanding just enforces wrong patterns consistently.
Salesforce owns the platforms, but most clients don’t have Data 360, Informatica, and MuleSoft fully licensed, configured, and integrated. You’re building this architecture with whatever tools they already own plus what you can convince them to buy.
Start with minimum viable context architecture. One core entity, one critical signal, basic validation. Get the agent working with limited but accurate context, then expand systematically. Clients can’t wait three months for their agent to work. They need results in weeks.
Scope creep kills these projects. Clients imagine agents that know everything about their business on day one. Build context incrementally, validate each layer, then add the next.
Getting Started
If you’re facing your first Agentforce implementation, start here:
Week 1-2: Map your client’s core business entities across systems. Pick customers, products, and orders. Understand how each system defines them and where the mismatches exist.
Week 3-4: Identify the top three operational signals that impact agent decisions. For most companies, this is inventory updates, customer status changes, and order fulfillment milestones. Build event capture for these first.
Week 5-6: Implement basic data quality checks on the objects and signals you’ve mapped. Focus on catching issues that break agent functionality, not achieving perfection.
Week 7-8: Deploy a limited agent that uses this context architecture. One use case, narrow scope. Validate the approach works before expanding.
Budget planning matters too, understanding how Agentforce flex credits work helps you set realistic expectations with clients about ongoing costs.
The Reality
Building context architecture is harder than configuring Salesforce. It requires cross-functional coordination, stakeholder alignment on data definitions, and ongoing maintenance as systems change.
This is why the best engineers are becoming forward-deployed consultants. The work requires both technical depth and client-facing problem-solving skills that traditional developer or consultant roles don’t develop.
But it’s the only way Agentforce implementations survive contact with production. Salesforce gave you the platforms. The integration work is yours.
We’ve been building AI-first delivery practices for over a year now at realfast. Multiple industries, different tech stacks. The pattern holds: context architecture determines whether agents work or fail.
Our work with TASC Outsourcing showed how proper context architecture tripled their email response rates.
Salesforce’s $8 billion bet validates what we’ve been saying. The question isn’t whether context matters. It’s whether you’re building it systematically or hoping agents figure it out on their own.
They won’t.
Need help building this for your client? We’ve systematized context architecture for Agentforce across fintech, education, recruitment, and hospitality implementations.
If you’re facing a complex brownfield deployment or need to accelerate delivery timelines, book a demo to see how we approach these projects.