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Source: internal_wiki/agentforce_architecture.md

Agentforce is Salesforce's AI agent platform built on the Einstein 1 Platform. It enables organizations to build, customize, and deploy autonomous AI agents that can take actions across Salesforce applications. Key components include: the Agent Builder (low-code configuration interface for defining agent behavior), Atlas Reasoning Engine (the AI brain that plans multi-step actions, handles ambiguity, and executes workflows), and Topics/Actions (the building blocks defining what agents can do and when). Agents operate in both customer-facing scenarios (service agents, commerce agents) and employee-facing scenarios (sales coaches, analytics assistants). Built-in guardrails enforce trust boundaries — agents cannot access data outside their permission set, and every action is auditable. The platform supports both declarative (clicks) and programmatic (Apex, Flow) customization.

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Source: internal_wiki/data_cloud_overview.md

Salesforce Data Cloud is a hyperscale data platform that unifies all customer data in real time. It ingests data from any source — CRM records, web analytics, mobile app events, IoT telemetry, third-party data providers, and legacy databases — then harmonizes it into a unified customer profile using identity resolution algorithms that match records across sources even when identifiers differ. Key capabilities: zero-copy data federation with Snowflake, Databricks, and BigQuery (query external data without moving it), real-time streaming ingestion via Apache Kafka connectors, calculated insights (derived metrics computed on the fly), segmentation engine for marketing audiences, and data actions that trigger automations when profile conditions change. Data Cloud processes over 36 trillion records quarterly and supports sub-second query latency on unified profiles.

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Source: internal_wiki/agentforce_data_cloud_integration.md

When Agentforce agents are connected to Data Cloud, they gain access to the complete unified customer profile — not just CRM data, but every touchpoint across every channel. This enables: 1) Personalized agent responses grounded in full customer history (purchase patterns, support cases, engagement signals), 2) Real-time context from interactions happening right now across web, mobile, and in-store channels, 3) Predictive insights from calculated fields and Einstein AI models (churn propensity, lifetime value, next best action), 4) Semantic search across unstructured knowledge bases using Data Cloud's vector database for RAG (Retrieval Augmented Generation). The integration architecture uses Data Cloud as both the retrieval layer (vector search for knowledge) and the context layer (unified profile for personalization). Agent prompts are dynamically assembled: system instructions + retrieved knowledge chunks + customer profile fields + conversation history.

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Source: support_tickets/recent/ticket_45123.md

Customer: Elevance Health (Fortune 25 healthcare company)
Priority: P1 — Production Impact
Issue: Agentforce service agent returning stale eligibility data. Members calling about coverage are getting answers based on data that's 15-45 minutes old. Expected real-time accuracy (< 30 seconds) per SLA.
Investigation: Data Cloud ingestion pipeline for health plan eligibility data was configured in batch mode (15-minute intervals) instead of real-time streaming mode. Additionally, the agent's "Eligibility Verification" topic was missing the "refresh_context" action — without it, the agent uses cached profile data from the start of the conversation rather than querying Data Cloud for the latest state.
Resolution: 1) Switched eligibility data source connector from batch to real-time streaming ingestion (Kafka). 2) Added "refresh_context" action to the Eligibility Verification topic, configured to fire before every eligibility response. 3) Added monitoring dashboard for ingestion lag. Result: latency reduced from 15-45 minutes to < 5 seconds. Customer confirmed fix on March 3, 2026.

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Source: internal_wiki/context_window_management.md

Context window management is the #1 operational challenge for Agentforce at scale. Each agent conversation consumes tokens from the model's context window, and complex multi-turn conversations with enterprise customers can involve: large customer profiles (50-200 fields), conversation history (10-30 turns), retrieved knowledge chunks (3-10 chunks at 500-2000 tokens each), system instructions, and tool/action schemas. A single complex conversation can consume 15,000-30,000 tokens of context. Current mitigation approaches: 1) Selective field inclusion — only send customer profile fields relevant to the current topic (reduces profile from 8K to 1-2K tokens), 2) Conversation summarization — after 10 turns, compress earlier turns into a summary (saves ~60% but loses nuance), 3) Chunked retrieval with re-ranking — retrieve top-5 chunks, re-rank, keep top-3 (reduces knowledge context by 40%). These are all manual optimizations configured per-agent. A compression middleware that could automatically reduce token usage by 50-70% while maintaining response quality would: a) reduce per-conversation cost by 50-70%, b) enable longer conversations before hitting context limits, c) allow more knowledge chunks and profile data per turn, d) let smaller/cheaper models handle conversations that currently require frontier models.

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Source: competitive_intel/agent_platforms_q1_2026.md

AI Agent Platform Competitive Landscape — Q1 2026:

Microsoft Copilot Studio: Strong enterprise penetration via M365 bundle. Deep Azure AI integration. Weakness: complex setup, poor CRM-native data access. Market share: ~30% enterprise.

Google Vertex AI Agents: Best for GCP-native shops. Strong on multimodal (vision, audio). Gemini 2.0 Flash is price-competitive. Weakness: limited pre-built connectors, enterprise trust narrative is weaker. Market share: ~15%.

AWS Bedrock Agents: Most flexible model choice (Claude, Llama, Mistral, Titan). Cost-effective for high-volume use cases. Weakness: requires significant custom development, no native CRM. Market share: ~20%.

ServiceNow Now Assist: Dominant in ITSM/ITOM agent use cases. Deep ServiceNow platform integration. Weakness: narrow focus, doesn't compete well outside IT workflows. Market share: ~10% (in IT agent segment: ~45%).

Salesforce Agentforce: CRM-native advantage is massive for customer-facing agents. Trust layer (Einstein Trust Layer) is best-in-class for compliance. Data Cloud integration provides richest customer context. Weakness: higher cost per conversation than open-source alternatives, vendor lock-in concerns, limited model choice (primarily OpenAI partnership). Market share: ~25% enterprise CRM-adjacent agents.

Emerging Open Source: CrewAI (multi-agent orchestration, 40K GitHub stars), LangGraph (stateful agent graphs, LangChain ecosystem), AutoGen (Microsoft Research, strong at code generation agents), Camel-AI (role-playing agent framework). These are developer-focused and gaining traction in startups but lack enterprise trust/compliance features.
