SARP Framework
Agent-Ready Data Platforms. As AI agents become integral to enterprise operations, data platforms need to evolve. SARP is a practical framework for making your data infrastructure agent-ready — incrementally, without ripping and replacing.
From human-scale to agent-scale data access
From human-scale to agent-scale
Your data platforms were designed for human access patterns: analysts running complex queries a few times per day, dashboards refreshing on schedules, ad-hoc exploration with tolerance for latency. AI agents access data differently — and the gap is real.
Human queries
Complex, infrequent, tolerant of latency. Analysts understand implicit context and can interpret ambiguous results.
Agent queries
Frequent, focused, demanding sub-second response. Agents need explicit semantic context to avoid hallucination.
The opportunity
This isn't a crisis — it's an evolution. Organizations that close the gap early gain competitive advantage in AI deployment.
Where current platforms fall short
None of these are insurmountable. But they require intentional design.
Query pattern mismatch
Human queries are complex and infrequent. Agent queries are simple and continuous. Result: query queues, timeouts, throttling when agents hit platforms designed for human patterns.
Missing context layer
Humans bring implicit understanding to data. Agents need explicit semantic context. Without it, they hallucinate and misinterpret. Your platform returns data; agents need intelligence.
API limitations
Current interfaces: SQL, JDBC, batch-oriented exports. What agents need: semantic APIs, streaming access, natural language. Result: brittle integrations, high maintenance burden.
Scale constraints
Platforms sized for dozens of concurrent users hit limits when hundreds of agents query in parallel. Capacity limits are reached earlier than expected.
"SARP provides a structured approach to agent-readiness — incrementally, with quick wins along the way."
Four dimensions of agent-readiness
SARP provides a structured approach across four dimensions, each with practical components and quick wins.
Dimension 1: Access Layer
Making data accessible in agent-native ways.
Components
Semantic Query API, context-enriched responses, streaming interfaces, rate-aware design.
Quick wins
Add a semantic search layer (vector embeddings), implement query result caching, create agent-specific API endpoints.
Dimension 2: Context Infrastructure
Ensuring agents have the context they need.
Components
Metadata catalog, data lineage, semantic annotations, quality signals (freshness, completeness, reliability).
Quick wins
Document most-queried tables with semantic descriptions, add freshness timestamps to responses, create a business term glossary.
Dimension 3: Scale Architecture
Handling increased query volume efficiently.
Components
Horizontal scaling, intelligent caching (agent-pattern aware), query prioritization, cost controls per agent/use case.
Quick wins
Implement Redis/Memcached for frequent queries, set up query cost monitoring, create agent-specific connection pools.
Dimension 4: Governance
Maintaining control as agents proliferate.
Components
Agent authentication, access policies, audit trail, usage analytics.
Quick wins
Require API keys for agent access, log all agent queries with metadata, set up basic dashboards for agent activity.
SARP maturity levels
Most organizations are at Level 1-2 today. Level 3-4 provides significant competitive advantage. Level 5 is emerging best practice.
Level 1: Ad-Hoc
Agents access data through generic APIs. No agent-specific optimization. Limited visibility into agent usage. Typical starting point for most organizations.
Level 2: Aware
Basic API access designed for agents. Some caching for repeated queries. Agent activity logging. Manual context documentation.
Level 3: Optimized
Semantic query interfaces. Agent-pattern caching strategies. Automated context enrichment. Query prioritization in place.
Level 4: Agent-Ready
Full SARP implementation. Self-service agent onboarding. Real-time observability. Cost attribution and controls.
Level 5: Agent-Native
Data platform designed agents-first. Bi-directional agent-data feedback loops. Predictive scaling based on agent patterns. Continuous optimization.
Where SARP delivers value
RAG Applications
Retrieval-Augmented Generation needs fast, contextual data access. SARP's semantic query layer + context metadata = better retrieval, less hallucination, reduced prompt engineering.
Autonomous Agents
Agents making decisions need reliable, fresh data. Real-time APIs + quality signals = trustworthy agent decisions with appropriate guardrails.
Multi-Agent Systems
Multiple agents querying simultaneously with different needs. Prioritization + caching + rate limiting = stable, predictable performance as agent count grows.
Customer-Facing AI
Chatbots and assistants need instant data access. Sub-second APIs + semantic search = responsive experiences, reduced escalation to humans.
How we help
SARP Assessment
$25K
2 weeks. Current state evaluation across all dimensions, maturity scoring, gap analysis, prioritized roadmap with quick wins.
SARP Quick Start
$40K
4 weeks. Focus on one high-value use case, implement 3-5 quick wins, demonstrate measurable improvement, build internal momentum.
SARP Architecture
$60-90K
4-6 weeks. Target state architecture design, technology recommendations, migration strategy, implementation plan.
SARP Implementation
$150-400K
3-6 months. Access layer implementation, context infrastructure, scale architecture, governance framework, team enablement.
Is your data platform agent-ready?
Most organizations don't know where they stand. Our SARP Assessment gives you a clear picture in two weeks — maturity scores, gaps, and a prioritized roadmap.