Resource and Cost Optimization - Agentic Patterns

Learn more about Well-Architected Resource and Cost Optimization → Agentic Enterprise Resource and Cost Optimization

Patterns

Where to lookWhat good looks like
Agentforce | Action✅ Design Actions accepting collections. Agent processing 50 case updates invokes single Action with collection of case IDs, not 50 separate invocations
Agentforce | Action✅ Use relationship queries retrieving parent and child data in single SOQL statements to manage SOQL budget across Action chains
Platform | Platform Cache✅ Cache reference data in Platform Cache eliminating repeated queries across Actions within same conversation
Platform | Apex✅ Move agent operations exceeding synchronous limits to Queueable or Batch Apex.
Platform | Apex✅ Profile Actions under load with Apex execution logs. Move compute-heavy processing to async context when approaching synchronous limit
Agentforce | Action✅ Design Actions with hard child-record limits and pagination for data skew scenarios
Agentforce | Action✅ Implement sampling when volumes exceed thresholds for agents that may request “all history” unpredictably
Platform | Scale Center✅ Use Scale Center transaction traces to identify which Actions consume excessive queries

Anti-Patterns

Where to lookWhat bad looks like
Agentforce | Action⚠️ Invoking separate Actions for each record (e.g., 50 separate calls for 50 case updates) instead of accepting collections
Platform | Apex⚠️ Running 5-10 SOQL queries per Action in complex workflows of 10-15 Actions, approaching the 100 synchronous SOQL limit
Platform | Apex⚠️ Running large processing synchronously in an Action instead of in an async context (e.g., scoring 1,000+ leads in synchronous Apex)
Agentforce | Action⚠️ Allowing agents to retrieve unlimited child records from accounts with 15,000 opportunities or cases with 5,000 activities without defensive coding

Patterns

Where to lookWhat good looks like
Platform | Event Monitoring✅ Monitor API consumption in Event Monitoring during pilot phase to understand actual usage before full rollout
Data 360 | Configuration✅ Set top-k limits on vector search to retrieve only the number of results actually used
Data 360 | Analytics✅ Monitor vector search result utilization in Data 360 Analytics for tuning of top-k parameters
Platform | Platform Events✅ Publish Platform Events when conditions require agent intervention rather than scheduled polling consuming API calls regardless of work existence
Integration | Action✅ Use composite patterns: create a single MuleSoft experience API to aggregate multiple system checks (inventory, pricing, credit) in parallel, agent invokes 1 external Action consuming 1 callout
Integration | Action✅ Design single MuleSoft flows that execute all downstream updates rather than multiple sequential flows per agent interaction
Agentforce | Agent Configuration✅ Embed agents in Lightning pages using standard components to avoid consuming API calls per interaction
Business | Planning✅ Evaluate MuleSoft licensing models (transaction-based vs platform licensing) against projected agent transaction volumes to determine best economics

Anti-Patterns

Where to lookWhat bad looks like
Data 360 | Configuration⚠️ Retrieving top 50 vector search results when using only top 5, wasting 90% of compute and context budget
Platform | Scheduled Jobs⚠️ Scheduled polling consuming API calls regardless of work existence (e.g., checking every 5 minutes finding no work 80% of time)
Integration | Named Credentials⚠️ Using 3 separate Actions consuming 3 callouts instead of a single composite API aggregating multiple system calls
Platform | Apex⚠️ Custom LWCs making explicit API requests consuming API calls when standard Lightning components could avoid this
Integration | External Systems⚠️ External systems polling Salesforce for state changes consuming hundreds of daily API calls instead of subscribing to CDC events
Integration | Action⚠️ Making sequential API calls for each step (query customer, query products, check inventory, create order, update inventory, notify shipping) consuming 12+ calls per transaction when composite patterns would reduce consumption 75%
Integration | Agent Configuration⚠️ Orchestrating agent actions across many systems without evaluating whether each system interaction is necessary, multiplying API consumption with each added system

Patterns

Where to lookWhat good looks like
Agentforce | Prompt Template✅ Minimize prompt length: remove verbose instructions, redundant examples, unnecessary context. Selective context improves both latency and quality
Platform | Apex✅ Use selective SOQL with indexed filters. Leverage Query Plan Tool identifying missing indexes causing full table scans
Agentforce | Agent Configuration✅ Use parallel Action execution when no dependencies exist. Retrieving account details and opportunities simultaneously halves latency versus sequential retrieval
Platform | Platform Cache✅ Cache frequently accessed reference data (product catalogs, territory mappings, pricing rules) eliminating repeated SOQL
Platform | Platform Cache✅ Implement semantic caching matching similar queries to serve repeated requests without new inference
Platform | Apex✅ Process non-urgent requests asynchronously (status updates, batch processing, report generation) using generous async governor limits
Platform | Apex✅ Profile Actions under realistic data volumes. An Action taking 3s becomes bottleneck regardless of fast LLM inference
Agentforce | Action✅ Implement bulkification in Actions to minimize execution time per operation

Anti-Patterns

Where to lookWhat bad looks like
Agentforce | Prompt Template⚠️ Including exhaustive context that adds noise instead of selective context that improves both latency and quality
Agentforce | Agent Configuration⚠️ Executing Actions sequentially when no dependencies exist (e.g., account query 2s + opportunities query 2s = 4s sequential instead of 2s parallel)
Platform | Apex⚠️ Running full table scans due to missing indexes instead of using selective SOQL with indexed filters
Platform | Platform Cache⚠️ Querying Data 360 repeatedly (1.5s per call) for common data that could be cached (50ms from Platform Cache)
Agentforce | Agent Configuration⚠️ Requiring synchronous completion for non-urgent operations (status updates, batch processing, report generation)

Patterns

Where to lookWhat good looks like
Data 360 | Configuration✅ Chunk knowledge articles, product docs, case history into semantically meaningful 250-750 word segments
Data 360 | Configuration✅ Validate chunk size against agent query patterns. Balance precision (smaller chunks) versus context completeness (larger chunks)
Data 360 | Configuration✅ Use embeddings matching agent query patterns. Validate selection against representative queries measuring retrieval precision and recall
Data 360 | Configuration✅ Enhance vector similarity with metadata filters ensuring business constraints (e.g., filter to active products excluding discontinued items regardless of semantic similarity)
Data 360 | Configuration✅ Use unified profiles aggregating customer data from multiple sources into single view. Access complete context (attributes, insights, engagement, predictions) in single query
Data 360 | Configuration✅ Enable Identity Resolution providing accurate cross-system customer matching
Data 360 | Configuration✅ Configure Data 360 instances in regions matching regulatory requirements for data residency compliance

Anti-Patterns

Where to lookWhat bad looks like
Data 360 | Configuration⚠️ Using overly large chunks that include irrelevant information diluting relevance in vector search results
Data 360 | Configuration⚠️ Relying solely on semantic similarity without metadata filters, returning discontinued or low-quality results
Data 360 | Configuration⚠️ Orchestrating multiple separate source queries instead of using Data 360 unified profiles for complete customer context
Data 360 | Configuration⚠️ Using embedding models that don’t match agent query patterns (e.g., document classification embeddings for question-answer similarity)
Data 360 | Configuration⚠️ Processing European customer data on non-European Data 360 instances, violating data residency requirements

Patterns

Where to lookWhat good looks like
Agentforce | Agent Configuration✅ Prioritize context explicitly: recent conversation history (highest), critical business data/record state/permissions (second), historical context (lowest, only when space allows)
Agentforce | Agent Configuration✅ After 10-15 turns, summarize early conversation into concise overview maintaining critical facts without full verbatim history
Data 360 | Configuration✅ Store extended context in Data 360 or custom objects rather than full history in every LLM call. Query on demand keeping prompts focused on current needs
Agentforce | Action✅ Return only essential fields from Action responses. Account retrieval returns fields relevant to current task, not all 50 fields
Agentforce | Agent Configuration✅ Design explicit prioritization logic versus arbitrary truncation for context window allocation

Anti-Patterns

Where to lookWhat bad looks like
Agentforce | Agent Configuration⚠️ Arbitrary truncation of context without explicit prioritization logic
Agentforce | Agent Configuration⚠️ Maintaining full verbatim conversation history beyond 10-15 turns, exhausting context budget
Agentforce | Agent Configuration⚠️ Including full history in every LLM call instead of storing extended context externally and querying on demand
Agentforce | Action⚠️ Returning all 50 fields from account retrieval when only a subset is relevant to the current task, consuming context budget
Agentforce | Agent Configuration⚠️ Allocating context budget without reserving sufficient space for response generation

Patterns

Where to lookWhat good looks like
Platform | Action✅ Develop centralized Action libraries covering record CRUD, approvals, notifications, reporting, validation. Platform teams maintain ensuring consistent behavior, security, performance
Agentforce | Agent Configuration✅ Create focused specialist agents with deep domain expertise (service agent for case management, sales agent for opportunity guidance) maintaining narrower context and clearer boundaries
Agentforce | Agent Configuration✅ Use Agent Builder orchestration coordinating specialist agents for cross-domain needs
Agentforce | Prompt Template✅ Maintain validated prompt patterns in Prompt Builder. Templates capture proven approaches for RAG query formation, multi-step reasoning, response formatting
Agentforce | Prompt Template✅ Version prompt templates enabling A/B testing of improvements
Data 360 | Configuration✅ Maintain shared knowledge bases in Data 360 serving multiple agents. Single product knowledge base grounds sales, service, and partner portal agents
Agentforce | Agent Configuration✅ Compose new agents from proven building blocks: define purpose, select Actions, configure prompts (days versus weeks for custom builds)

Anti-Patterns

Where to lookWhat bad looks like
Agentforce | Prompt Template⚠️ Creating ad-hoc prompts for each agent instead of reusing validated prompt template libraries
Agentforce | Action⚠️ Reimplementing Actions for each agent instead of using centralized reusable Action libraries
Agentforce | Agent Configuration⚠️ Building universal agents attempting all scenarios instead of focused specialist agents with deep domain expertise
Data 360 | Configuration⚠️ Duplicating knowledge bases per agent instead of sharing a single knowledge base that grounds multiple agents

Patterns

Where to lookWhat good looks like
Platform | Custom Metadata Types✅ Use Custom Metadata Types and Prompt Builder controlling behavior without code changes. Admins modify prompt templates and reasoning parameters without development cycles
Agentforce | Agent Configuration✅ Route subsets to experimental versions comparing quality metrics, satisfaction, task completion before full rollout (e.g., 10% to v2, 90% on v1)
Agentforce | Agent Configuration✅ Monitor validation period and maintain rollback capability if issues discovered during A/B testing
Agentforce | Action✅ Define clear interface contracts between components: Actions specify inputs, outputs, errors, performance expectations
Agentforce | Agent Configuration✅ Gradually expand successful versions (10% to 25% to 50% to 100%) over multi-week rollout periods
Agentforce | Action✅ Replace Action implementations without agent modifications when interface contracts are preserved

Anti-Patterns

Where to lookWhat bad looks like
Platform | Apex⚠️ Requiring code changes to modify agent behavior instead of using configuration-driven approaches (Custom Metadata Types, Prompt Builder)
Agentforce | Agent Configuration⚠️ Full rollout of new agent versions without A/B testing on a subset comparing quality metrics and satisfaction
Agentforce | Action⚠️ Agents depending on Action implementation details rather than interface contracts, breaking when implementations change
Agentforce | Agent Configuration⚠️ Deploying new versions to 100% of users immediately without graduated rollout
Agentforce | Agent Configuration⚠️ Operating without rollback capability during experimental version testing

Patterns

Where to lookWhat good looks like
Agentforce | Agent Configuration✅ Resolve each request in as few agent actions as possible — Flex Credits are consumed per action, so consolidating multi-step logic into a single action lowers cost directly
Agentforce | Agent Configuration✅ Model monthly cost projections based on documented Flex Credit consumption rates and expected interaction volumes enabling budget planning
Einstein | Monitoring✅ Monitor Flex Credit consumption by agent, use case, and user population to identify high-consumption patterns requiring optimization
Einstein | Monitoring✅ Compare actual consumption against projections to inform future planning and identify optimization opportunities

Anti-Patterns

Where to lookWhat bad looks like
Agentforce | Agent Configuration⚠️ Chaining many small actions where one consolidated action would resolve the request, multiplying Flex Credit consumption per interaction
Business | Planning⚠️ Discovering Flex Credit consumption exceeds projections only after production deployment because no per-interaction cost modeling was performed

Patterns

Where to lookWhat good looks like
Agentforce | Agent Configuration✅ Design session boundaries and timeouts so a conversation persists appropriately without terminating prematurely and forcing a new billable conversation
Agentforce | Agent Configuration✅ Focus agent capability on first-conversation resolution so a task completes within the initial conversation rather than requiring a billable follow-up
Einstein | Monitoring✅ Track first-conversation resolution rate as the cost-efficiency metric under Per-Conversation pricing
Agentforce | Agent Configuration✅ Handle related issues within the existing conversation context rather than opening separate conversations that each incur a flat fee

Anti-Patterns

Where to lookWhat bad looks like
Agentforce | Agent Configuration⚠️ Terminating conversations prematurely through short session timeouts, forcing users to start new billable conversations to finish a single task
Einstein | Monitoring⚠️ Monitoring only total conversation volume without tracking first-conversation resolution rate, missing the primary cost lever under Per-Conversation pricing

Patterns

Where to lookWhat good looks like
Business | Documentation✅ Drive adoption so licensed users actively engage the agent, since under unmetered Per-User pricing value comes from usage rather than from minimizing each interaction
Einstein | Monitoring✅ Track utilization to identify low-usage licensed users for targeted training or license reallocation
Business | Documentation✅ Connect agent interactions to business outcomes by user population so high-value use cases justify the per-seat investment
Business | Planning✅ Review user populations on a cadence so licenses stay aligned with actual usage rather than drifting into over-licensing

Anti-Patterns

Where to lookWhat bad looks like
Business | Planning⚠️ Provisioning Per-User licenses without an adoption plan, paying fixed per-seat cost for users who rarely engage the agent
Einstein | Monitoring⚠️ Never reviewing utilization by user population, letting licenses drift into the same over-licensing that wastes traditional seats

Patterns

Where to lookWhat good looks like
Agentforce | Agent Configuration✅ Implement triage agents that handle initial routing with simple logic, directing users to a specialized agent only when complexity requires it
Platform | Flow✅ Fall back to Flow automation for deterministic scenarios where rules-based logic suffices, reserving agent reasoning for the ambiguous situations that genuinely need it
Data 360 | Agent Configuration✅ Retrieve grounding data selectively, invoking vector search and document retrieval only when the agent’s reasoning requires it rather than on every interaction

Anti-Patterns

Where to lookWhat bad looks like
Agentforce | Agent Configuration⚠️ Routing every request straight to a specialized agent, incurring expensive complex invocations for requests that simple triage logic could have handled
Platform | Flow⚠️ Using agent reasoning for deterministic rules-based workflows that a Flow would execute at lower cost without variable consumption
Data 360 | Agent Configuration⚠️ Retrieving grounding data on every interaction regardless of whether reasoning requires it, driving Data 360 consumption that doesn’t track real need

Patterns

Where to lookWhat good looks like
Data 360 | Agent Configuration✅ Cache product catalog embeddings with defined refresh cadence (e.g., 24-hour), retrieving from cache for routine questions and querying Data 360 only for new or customer-specific data
Data 360 | Agent Configuration✅ Implement caching strategies to reduce repeated queries for common requests, balancing cost optimization against data freshness requirements
Data 360 | Org✅ Model upfront data ingestion and processing costs (harmonization, identity resolution, embedding generation) separately from incremental growth costs
Data 360 | Agent Configuration✅ Evaluate whether RAG overhead justifies quality improvements for specific use cases before adding retrieval-augmented generation

Anti-Patterns

Where to lookWhat bad looks like
Data 360 | Agent Configuration⚠️ No caching implemented despite the majority of queries being identical common questions, consuming Flex Credits for repeated identical responses
Data 360 | Org⚠️ Implementing aggressive caching without considering data freshness requirements, serving stale information to users

Patterns

Where to lookWhat good looks like
Platform | Org✅ Use Partial Copy sandbox with targeted customer data sample for agent development, reserving Full Copy sandbox exclusively for pre-production validation
Platform | Org✅ Plan for development infrastructure costs upfront including Data 360 capacity, Flex Credits for prompt testing, and integration connectivity

Anti-Patterns

Where to lookWhat bad looks like
Platform | Org⚠️ Every developer provisioning Full Copy sandboxes for agent development, multiplying sandbox costs 5x
Platform | Org⚠️ Relying on ad-hoc manual testing providing insufficient coverage, discovering quality issues only in production
Business | Planning⚠️ Making development infrastructure costs invisible during planning, resulting in significant unexpected costs during implementation

Patterns

Where to lookWhat good looks like
Business | Documentation✅ Build comprehensive TCO model covering all six cost categories: development, inference (Flex Credits), infrastructure (Data 360 + MuleSoft), operations, governance, and change management
Business | Documentation✅ Create spreadsheet TCO models projecting 3-5 year costs with documented assumptions for interaction volumes, credit consumption rates, and growth trajectories
Business | Documentation✅ Perform sensitivity analysis revealing which assumptions most affect total investment, enabling focused validation efforts
Business | Documentation✅ Model ongoing operational costs as a meaningful recurring fraction of development costs annually for mature agents, drawn from general AI-agent industry benchmarks rather than a fixed Salesforce figure
Business | Planning✅ Include change management costs (user training, business process adaptation, organizational change) which are frequently underestimated

Anti-Patterns

Where to lookWhat bad looks like
Business | Planning⚠️ Including only development cost in agent business case without modeling ongoing consumption (Flex Credits, Data 360, operations), discovering significant unplanned annual costs after deployment
Business | Planning⚠️ Omitting governance overhead (safety reviews, human oversight infrastructure, audit logging, compliance validation) which grows with agent autonomy and risk level
Business | Planning⚠️ Underestimating change costs especially for agents creating significant workflow shifts, leading to budget shortfalls

Patterns

Where to lookWhat good looks like
Business | Documentation✅ Evaluate pre-built Agentforce agents versus custom development through comprehensive 3-year TCO comparison including maintenance costs
Business | Documentation✅ Document build vs buy decisions in Architecture Decision Records enabling future reassessment as costs and capabilities change
Agentforce | Agent Configuration✅ Consider customization of pre-built agents through Prompt Builder and action configuration as middle ground between out-of-box and fully custom
Business | Planning✅ Factor decision criteria beyond cost: time-to-value, organizational development capability, strategic differentiation value, and vendor dependency tolerance

Anti-Patterns

Where to lookWhat bad looks like
Business | Planning⚠️ Building custom agent because “we want control” without modeling 3-year TCO showing pre-built option may be significantly cheaper
Business | Planning⚠️ Choosing build or buy based solely on first-year costs without projecting ongoing maintenance, credit consumption, and vendor update benefits over multi-year horizon

Patterns

Where to lookWhat good looks like
Business | Documentation✅ Compare agent TCO against traditional automation alternatives (Flow, Apex, manual processes) before deployment, validating investment justification
Business | Documentation✅ Establish manual process baseline including fully-loaded employee costs, error rates, processing time, and throughput capacity to reveal potential savings
Agentforce | Agent Configuration✅ Deploy agents for tasks requiring natural language understanding, adaptive reasoning, or handling high scenario variability where traditional automation cannot match capability
Platform | Flow✅ Use Flow automation for deterministic workflows with clear rules, providing equivalent outcomes at lower cost without variable inference consumption

Anti-Patterns

Where to lookWhat bad looks like
Agentforce | Agent Configuration⚠️ Deploying agent for simple deterministic routing task consuming $15K annual credits when Flow would provide equivalent functionality with no variable costs
Business | Planning⚠️ Defaulting to agent approach for all automation without evaluating whether traditional automation provides equivalent outcomes at lower total cost

Patterns

Where to lookWhat good looks like
Einstein | Monitoring✅ Build Flex Credit consumption dashboards showing consumption by agent, user population, time period, and interaction type for rapid identification of consumption spikes
Einstein | Monitoring✅ Implement per-interaction cost attribution connecting credit consumption to business transactions (cost per resolved case, cost per qualified lead, cost per processed order)
Einstein | Monitoring✅ Configure budget alerting at example thresholds such as 70% and 85% by agent and use case (not only organization-wide) enabling proactive intervention before overruns
Einstein | Monitoring✅ Implement anomaly detection identifying unusual consumption patterns (sudden spikes indicating prompt inefficiencies, unexpected usage patterns, or abuse)
Business | Documentation✅ Share cost dashboards with business stakeholders and technical teams creating transparency enabling cost-aware agent usage
Business | Documentation✅ Conduct quarterly budget reviews comparing actual consumption against projections, evaluating business outcomes, and adjusting allocations based on demonstrated value
Business | Planning✅ Reserve 10-15% of credit budget for experimentation and unexpected consumption growth, preventing innovation bottlenecks
Business | Planning✅ Create multi-year projections based on agent adoption trends, new deployments, and expanding use cases enabling capacity planning and vendor negotiations

Anti-Patterns

Where to lookWhat bad looks like
Einstein | Monitoring⚠️ Monitoring credit consumption only through monthly invoice review, discovering budget overruns 6 weeks into quarter without time to optimize before renewal
Einstein | Monitoring⚠️ Configuring budget alerts only at the organization level, missing per-agent and per-use-case consumption spikes that indicate optimization opportunities
Business | Planning⚠️ Requiring emergency budget increases because consumption monitoring was not implemented early enough to identify and address overruns proactively
Business | Planning⚠️ Setting an annual budget once at fiscal year start without reviews, starving high-value agents for credits while low-ROI agents consume the allocation
Business | Planning⚠️ Allocating zero budget reserve for experimentation, forcing teams to justify every new use case through lengthy approval before any exploration

Patterns

Where to lookWhat good looks like
Einstein | Monitoring✅ Track consumption by agent instance enabling comparison across agent types and use cases, revealing which agents deliver best ROI
Business | Documentation✅ Connect consumption to business processes (lead qualification, case resolution, order processing) enabling business leaders to evaluate strategic alignment
Einstein | Monitoring✅ Track consumption by department, role, or user segment revealing where agent adoption is highest and where costs concentrate
Business | Planning✅ Use quarterly cost reviews to compare agent ROI across portfolio and reallocate investment from low-ROI to high-ROI agents based on demonstrated value

Anti-Patterns

Where to lookWhat bad looks like
Business | Planning⚠️ Allocating all agent costs to IT budget without business unit attribution, preventing business leaders from evaluating whether spending aligns with priorities
Business | Planning⚠️ Treating agent consumption as undifferentiated cost without connecting consumption to business outcomes, making ROI calculation impossible

Patterns

Where to lookWhat good looks like
Business | Documentation✅ Implement showback reporting providing consumption visibility by business unit, creating cost awareness enabling informed usage decisions without contentious chargeback disputes
Business | Documentation✅ Use hybrid allocation with chargeback for high-volume production agents and showback for experimental or low-volume agents, balancing accountability with innovation flexibility
Business | Documentation✅ Conduct quarterly reviews discussing whether consumption aligns with value delivered, leading to optimization commitments

Anti-Patterns

Where to lookWhat bad looks like
Business | Planning⚠️ Implementing full chargeback for experimental agents, discouraging innovation and exploration by immediately imposing budget accountability on nascent use cases

Patterns

Where to lookWhat good looks like
Agentforce | Prompt Template✅ Eliminate unnecessary verbosity in system prompts recognizing every extra token adds prefill compute and latency across all interactions — trim system prompts to what the agent actually uses
Agentforce | Prompt Template✅ Include only relevant information through selective context retrieval with relevance ranking ensuring highest-value information fits within context limits
Agentforce | Prompt Template✅ Specify concise response formatting for routine interactions, minimizing completion tokens by avoiding requests for elaborate formatting or extensive explanations
Agentforce | Prompt Template✅ Reuse standardized prompt templates across similar agents, amortizing prompt optimization investment while creating consistency and efficiency

Anti-Patterns

Where to lookWhat bad looks like
Agentforce | Prompt Template⚠️ Writing prompts conversationally without optimization consuming 2K+ tokens per interaction including verbose instructions and extensive examples that agents rarely reference
Agentforce | Prompt Template⚠️ Retrieving all potentially-related data for context rather than selectively including only relevant information, inflating prompt token consumption
Agentforce | Prompt Template⚠️ Creating unique prompt templates for every agent rather than standardizing patterns, multiplying prompt engineering effort without proportional quality benefit

Patterns

Where to lookWhat good looks like
Agentforce | Agent Configuration✅ Cache common agent responses for frequently-asked questions serving directly from cache for duplicate queries (cache hit rates above 30% indicate significant optimization opportunity)
Data 360 | Agent Configuration✅ Cache stable knowledge context (product catalogs, policy documents, reference materials) reducing retrieval overhead for every interaction referencing common information
Agentforce | Agent Configuration✅ Implement conversation caching within user sessions maintaining context across turns without re-sending full history, reducing token consumption in multi-turn interactions
Agentforce | Agent Configuration✅ Define cache invalidation strategies based on content volatility (product information caches for hours/days, real-time data should not cache)

Anti-Patterns

Where to lookWhat bad looks like
Agentforce | Agent Configuration⚠️ No caching implemented despite 60% of queries being identical common questions, unnecessarily consuming Flex Credits for repeated identical responses
Agentforce | Agent Configuration⚠️ Caching real-time data (inventory levels, pricing, availability) that requires freshness, serving stale information that leads to incorrect agent responses
Agentforce | Agent Configuration⚠️ Never refreshing cached responses leading to outdated answers as products, policies, or processes change

Patterns

Where to lookWhat good looks like
Business | Documentation✅ Require business case including projected 3-year TCO, expected business outcomes with measurement methodology, comparison against alternatives, and risk assessment
Business | Documentation✅ Define approval thresholds that separate investments a department can authorize from those requiring executive approval, so higher-investment or higher-risk agents receive proportionate executive scrutiny
Business | Documentation✅ Mandate pilot deployments validating assumptions (consumption, quality, adoption) before full production investment
Business | Documentation✅ Include ROI calculation in agent proposals (e.g., 4:1 return demonstrated through cases resolved and manual handling cost savings)

Anti-Patterns

Where to lookWhat bad looks like
Business | Planning⚠️ Deploying agents without business case, discovering 3x projected credit consumption while delivering unclear business value, creating unplanned annual consumption without documented ROI
Business | Planning⚠️ Skipping pilot deployment and going directly to full production, missing opportunity to validate consumption assumptions before committing full budget
Business | Planning⚠️ Approving agent investments without comparison against alternative approaches (manual processes, traditional automation) that may deliver equivalent value at lower cost

Patterns

Where to lookWhat good looks like
Business | Documentation✅ Conduct annual portfolio review evaluating all agents comparing cost, usage, business value, and strategic alignment to reveal optimization opportunities missed by agent-by-agent review
Business | Documentation✅ Define sunsetting criteria (low adoption, poor ROI, superseded functionality, excessive cost) preventing accumulation of low-value agents consuming budget
Business | Planning✅ Practice continuous investment reallocation shifting resources from low-performing agents to high-value opportunities aligned with evolving business priorities
Business | Documentation✅ Categorize agents by ROI tier (e.g., top 2 deliver 70% value at 50% cost = excellent; middle 4 deliver 25% value at 35% cost = acceptable; bottom 2 deliver 5% value at 15% cost = sunset candidates)

Anti-Patterns

Where to lookWhat bad looks like
Business | Planning⚠️ Agents deployed remaining in production indefinitely without utilization or value review, accumulating low-value agents consuming budget that could fund higher-value opportunities
Business | Planning⚠️ Evaluating agents only individually without portfolio perspective, missing the insight that a few agents deliver most value while many consume disproportionate resources