EAHOP
EAHOP (Enterprise AI Agent Harness & Optimization Platform) is a production-deployed system on SAP BTP that autonomously analyzes, optimizes, and redeploys AI agents. It is the first platform to treat AI agents themselves as software artifacts subject to structured performance engineering — reducing costs while increasing accuracy without any human-written code changes.
The Problem
Enterprise AI agents deployed on SAP BTP exhibited inconsistent accuracy, high API costs, and lacked any systematic way to self-improve. Manual prompt engineering was slow, error-prone, and required expert intervention for each agent. Teams had no visibility into why an agent underperformed or which optimization levers mattered most.
The Solution
EAHOP introduces 6 novel optimization engines (UAR, Agent Genome, Digital Twin, OKG, OME, AOS) that analyze uploaded agent codebases, simulate behavior via digital twins, extract optimization opportunities through knowledge graphs, and deliver the winning variant as a GitHub PR. The platform evaluates 22 performance metrics and uses multi-armed bandit selection to choose the best-performing variant automatically.
Key Features
Challenges & Learnings
The core challenge was designing the Agent Genome representation — a structured JSON schema that captures agent intent, tool dependencies, prompt strategies, and behavioral contracts in a way that Claude 3.7 Sonnet can reason about and safely mutate. Ensuring that optimized variants remained semantically correct (not just syntactically valid) required the Digital Twin engine to simulate agent behavior against production-representative test cases before any PR was raised. Another challenge was the SAP BTP deployment: CORS policies, Cloud Foundry routing, and SAP AI Core's model-serving API required custom middleware and careful environment configuration.
Research Contributions
Introduces the concept of 'Agent Genome' — a structured behavioral fingerprint for AI agents analogous to a software design document but machine-readable and mutation-safe. The Ontological Knowledge Graph (OKG) engine represents the first formalization of agent optimization as a graph traversal problem. Results accepted for IEEE publication.
Business Impact
+17.6% average accuracy improvement and −11.6% API cost reduction across 5 production agents. Demonstrated ROI potential of >$40K/year for mid-scale SAP AI deployments. Zero manual code changes required by developers post-deployment.
Future Direction
Planned multi-agent coordination optimization (optimizing agent networks, not just individual agents), integration with SAP Integration Suite for cross-system agent orchestration, and a self-healing mode where EAHOP detects production degradation and automatically deploys a new optimized variant.
Tech Stack
Quick Links
Want to see more of my work?
Back to Projects