Agentic intelligence has the potential to transform every industry. But only when connected to relevant context.
The major LLMs many of us are familiar with: ChatGPT, Claude, Perplexity, and Gemini, are only so useful in the enterprise context. In order to handle complex tasks within a large organization, AI systems need more than individual prompts. As one CEO put it: “the problem at the heart of many AI disappointments isn’t bad code. It’s context starvation”.
Agents need context. But there are two blockers standing in the way. First, brittle, batched-based data infrastructure that cannot deliver fresh, up-to-date context so AI can act in the moment. Second, a secure, compliant way to connect agents to context, without overwhelming production systems.
Due to the non-deterministic nature of AI agents, we cannot know for certain how many times they query a source database. Enterprises therefore need continuous, real-time, compliant zones where agents can safely retrieve the vital context they need to produce meaningful outcomes.
Equipping Agents: The Challenges Behind Agentic AI at Scale
For AI agents to produce meaningful outcomes based on relevant insights, they need real-time, governed context delivered in AI-ready formats, without overwhelming core production systems.
- Stale or delayed context: If agents operate on stale, outdated information, they make flawed predictions, miss opportunities, and deliver unreliable outcomes. In enterprise environments, even small delays can lead to poor customer experiences, financial risk, or compliance failures.
- Unsafe or non-compliant context: Feeding agents ungoverned data introduces significant exposure, such as violating GDPR, CCPA, or AI governance rules. Beyond legal risk, unsafe data erodes trust in agentic decisions, undermining the organization’s confidence in their AI systems.
- Production system overload: Allowing agents to directly query live operational systems creates contention, latency spikes, and outages. This destabilizes mission-critical applications and hinders AI adoption, as teams hesitate to risk production performance.
How Striim Powers Agentic AI with Rich, Real-Time, Read-Only Context
Striim supplies agentic AI with live, governed, and read-only context, ensuring AI systems can reason and act without putting production environments at risk. By transforming operational data into secure, AI-ready context in sub-second timeframes, Striim enables enterprises to scale agentic AI safely and effectively.
With Striim’s real-time, MCP-ready operational data store, enterprises get:
- MCP AgentLink, a solution that delivers sub-second, secure replication to feed AI agents live data without impacting production systems
- Built-in AI and ML interoperability that support open data formats, enabling agentic systems to utilize real-time data
- Governance agents: Sherlock and Sentinel, that automate masking and protect sensitive data in real time
- Vector embedding agent: Euclid, that embeds intelligence directly into data streams in real time
- Anomaly detection agent: Forseer, that detects and flags inconsistencies before they make an impact
- Striim Co-Pilot: making it fast, easy, and safe to deploy robust, real-time pipelines
- Scalable, event-driven architectures that keep agents continuously supplied with the most relevant context
Benefit from Architecture Built For Agentic AI
Enterprises can no longer afford to treat AI as an experiment. With AI-centric architecture, organizations can operationalize agentic systems safely and at scale. By embedding compliance, governance, and automation into the data layer, enterprises accelerate time-to-value while reducing risk and strengthening confidence in AI-driven outcomes.
Agentic AI relies on continuous, high-quality context. With governed pipelines delivering compliant, real-time data, enterprises can move from pilots to production quickly, ensuring AI agents act on the most relevant, trusted information.
Compliance should never be an afterthought. AI-ready architectures enforce governance in motion, ensuring sensitive data is masked, anonymized, and secured before it ever reaches an AI system.
Meaningful outcomes from AI are only possible when built on a solid foundation of trust. By grounding agents in transparent, well-governed data pipelines, enterprises improve explainability and reliability of outputs, building confidence from executives to end-users.
Manual governance and auditing are expensive, slow, and error-prone. Automated compliance within the streaming architecture enforces policies at scale, eliminating overhead and reducing costly rework.
The real returns from AI come when it’s embedded into daily decisioning and operations. With enterprise-ready data foundations, organizations can safely deploy agents that optimize processes, detect risks, and personalize services in real time.
Agentic AI in Action: How UPS Protects Shipments and Drives AI-Powered Revenue Growth
United Parcel Service (UPS), a global leader in logistics and package delivery, faced increasing pressure to secure shipments and reduce fraudulent claims. Rising e-commerce volumes and package theft exposed operational vulnerabilities, while merchants and consumers demanded greater reliability and trust. UPS needed a way to analyze delivery risk in real time, strengthen fraud prevention, and ensure AI-driven logistics decisions were powered by accurate, governed data.
The Striim Solution
UPS Capital implemented Striim’s real-time data streaming into Google BigQuery and Vertex AI, powering its AI-Powered Delivery Defense™ solution. Striim enabled high-velocity, sub-second data ingestion, cleaning, enrichment, and vectorization in motion, making data instantly AI-ready for ML models and APIs.
- AI-Powered Delivery Defense™: Streams data into BigQuery and Vertex AI for real-time risk scoring and address confidence.
- Fraud Detection & Risk Management: Analyzes behavioral patterns to flag risky deliveries and reduce fraudulent claims.
- Instant AI-Ready Data: Cleans, enriches, and vectorizes data in motion, ensuring UPS can run advanced ML models without latency.
- Adaptive Defense Against Emerging Threats: Continuous vector generation strengthens defenses against evolving fraud and theft tactics.
The Results
- Enhanced customer experience through reliable, more secure deliveries
- Cost savings from a reduction in package theft and fraudulent claims
- Proactive, AI-powered risk management through predictive analytics
- Shipper and merchant protection with continuous monitoring and anomaly detection
- Enterprise-grade AI enablement, through Striim’s scalable AI-ready data foundation
Ready to take the next step, and explore agentic AI with Striim? Try Striim for Free, or Request a Demo to learn more.
