On a mission to reinvent the modern API
At EzLabs we envision a future where software systems are inherently adaptable—where microservices communicate through intelligent, self-healing interfaces rather than rigid, brittle APIs. We are addressing these problems by replacing static API layers with an Agentic Programming Interface, called AlphaPi, which will transform how microservices interact and evolve.
AlphaPi will cut the KTLO, tech debt, and Operational Excellence cost incurred by an Engineeriong by up to 90 percent and let engineers focus on shipping new features.
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Challenges & Our Innovative Solutions
We've developed a streamlined architecture to address the most common challenges with building modern apps.
Traditional APIs and Their Limitations
Traditional software APIs are inherently rigid: each endpoint expects fixed request and response formats, and any change—adding a new filter, modifying a field, or supporting a new data source—requires updating, testing, and redeploying code. This slow, code‑centric process leads to high maintenance overhead and delayed feature roll‑outs. Once deployed, these APIs remain inflexible, forcing clients to continuously adapt to schema or contract changes and creating brittle integrations that can break silently when upstream services evolve.
Why Current LLM‑Driven Approaches Fall Short
Many "intelligent" API solutions built on agent‑to‑agent or multi‑chain frameworks rely on LLMs for both understanding user intent and orchestrating tool calls, but they introduce new challenges: agents regenerate planning thoughts with every call, driving up token usage and cost; each decision point incurs its own LLM round‑trip, resulting in sluggish response times; intermediate outputs go unchecked, leading to hallucinations or incomplete data; and overlapping tool capabilities cause inconsistent, unpredictable behavior. Importantly, these approaches still layer on top of static, code‑based APIs without addressing the underlying schema inflexibility or maintenance burden.
Our Flexible, LLM‑Enabled Architecture
Schema‑Aware Adaptation
We provide the LLM with up‑to‑date schema context so it can dynamically translate user requests into the exact format downstream services require. Rather than rewriting code for every new filter or data change, you simply update the schema context, and the system adapts in minutes.
Modular, Validated Workflows
The LLM focuses on initial planning: breaking tasks into well-defined, isolated steps and selecting tools based on predefined rules. Each generated workflow is executed and its results validated; only proven workflows are cached for reuse. This modular approach makes errors easy to trace and guarantees that reused workflows are reliable.
Speed and Efficiency Through Caching
Validated workflows are stored and keyed by the structured request signature. When an identical request arrives, the system bypasses LLM planning and runs the cached workflow directly via high‑speed protocols like gRPC or REST. This delivers response times on par with traditional web apps while drastically cutting LLM token usage.
Consistent, Predictable Integrations
By embedding tool selections within each validated workflow, we eliminate the variability of probabilistic LLM decisions. Every request of a given type follows the same, preapproved sequence, ensuring uniform output quality and predictable behavior.
By shifting schema handling, planning, and validation into an LLM‑driven protocol—and decoupling it from static code—we deliver truly flexible, low‑latency, and accurate "API" functionality that evolves at the speed of your business.
Key Benefits
Faster Response Times
Workflow caching reduces latency by up to 90% for common requests.
Reduced Costs
Lower token consumption means significant savings on your AI operations.
Improved Accuracy
Validation ensures only proven plans are executed and reused.
API Flexibility
Schema-aware adaptation enables easy updates without rigid code changes.
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