•Research Team
Zetesis AI Research & Engineering Roadmap
A comprehensive look at our planned milestones across autonomous workflows, evaluation benchmarks, and community ecosystem.
#Roadmap#Agent
Read ArticleARCHITECTURES & IMPACT STORIES
Stories of impact, scale,
and autonomous intelligence.
Discover how developers, research institutes, and enterprises leverage the open-source Zetesis ecosystem to orchestrate autonomous agents, index complex monorepos, and deploy verifiable AI systems.
Featured StoryAutonomous Systems
Engineering deterministic swarms: How distributed engineering teams achieve 99.4% task completion with Zetesis Agent Platform
By replacing brittle prompt chains with a state-machine execution kernel, teams eliminate tool-invocation regressions and run automated agent refactoring across complex production services.
99.4%
Execution Determinism
10x
Faster Context Convergence
99.4%
State Determinism
Guaranteed execution state convergence across multi-turn agent rollouts.
10x
AST Graph Traversal
Faster context retrieval across monorepos exceeding 1M+ lines of code.
10,000+
Verification Cases
Empirically tested benchmarks assessing reasoning and tool invocation.
100%
Open Architecture
POSIX-compliant sandboxes and model-agnostic orchestration.
Architectures & Implementations
System Case Studies
Agent FrameworkActive
CASE STUDY
Zetesis Agent Platform
High-performance multi-agent reasoning framework optimized for autonomous coding and complex workflows.
Knowledge RetrievalBeta
CASE STUDY
Zetesis Knowledge Engine
Hybrid vector and graph-based retrieval engine tailored for codebases and enterprise knowledge bases.
Benchmarking & EvalResearch
CASE STUDY
Zetesis Eval Suite
Comprehensive benchmarking harness for measuring reliability, hallucination rates, and task completion in LLM agents.
"Zetesis allowed our team to transition from fragile one-shot prompting to verifiable, deterministic multi-agent orchestration. The reduction in regression rates is night and day."
Elena Rostova
Lead Systems Architect, Distributed Systems Lab
"The AST-native code graph indexer is the first tool that reliably provides our coding agents with exact call topologies instead of noisy semantic approximations."
Marcus Chen
VP of Engineering, CloudCore Infrastructure
Architectural Pillars
Engineered for Mission-Critical Complexity
Transcending simple prompt chains. We re-engineer how agents plan, inspect tools, execute code, and verify factual assertions.
Orchestration
Autonomous Swarm Orchestration
Hierarchical task planning, extended context persistence, asynchronous event streaming, and deterministic state-machine execution.
Code RAG
AST-Native Code Cartography
Combining abstract syntax tree topology with hybrid semantic & graph retrieval to supply pinpoint contextual awareness for large monorepos.
Verification
Empirical Verification Engine
Rejecting black-box intuition. Standardized test suites for hallucination elimination, automated rollback, and task reproducibility.
Performance
Ultra-Low Latency Kernel
Engineered in modern Rust & TypeScript with zero bloated overhead, designed to embed seamlessly in edge runtimes and cloud swarms.
Dev Experience
Developer-First Toolchain
POSIX-compliant sandboxes, standardized CLI utilities, declarative YAML agent specs, and automated CI/CD integration.
Open Ecosystem
Model-Agnostic Hybrid Routing
Seamlessly distribute inference across frontier proprietary models and specialized open-source weights with zero vendor lock-in.
Dispatches & Papers
Research & Publications
•Zetesis AI Team
Welcome to Zetesis AI
Announcing the launch of Zetesis AI project official website and our mission to explore intelligent systems.
#Announcement#Vision
Read ArticleJoin the Inquiry
Build with the Zetesis Open Ecosystem
Whether orchestrating multi-agent collectives, integrating AST code cartography, or contributing empirical benchmarks, we welcome builders worldwide.