Superagentic AI
Superagentic AI is a research-led developer-tools company building tools, frameworks and infrastructure for safe, auditable, human-aligned agentic systems. Its research explores quantum AI through SuperQX while its products help teams design, optimize, evaluate and deploy production-grade agents.
At a Glance
- Individual developers and agent architects
- Infrastructure builders and engineering teams
- Technology teams and startups
- Enterprise engineering, research and innovation teams
- +1 more
AI Tools by Superagentic AI
(1)
SuperQode
Open Source Coding Agent Harness
Discussions
No discussions yet
Be the first to start a discussion about Superagentic AI
Latest News
SuperQode for macOS is now in public beta
Superagentic AI organised the first Agent Engineering Conference in London
Released a native Harness² implementation inside the CodeGem harness in SuperQode
Introduced CodeGem, a Gemini and Gemma coding harness in SuperQode
Products & Services
Open-source harness layer for coding agents. It discovers, builds, runs, evaluates and optimizes coding-agent harnesses from one terminal, with HarnessSpec, Harness Hub, durable WorkOrders, sessions, evidence and interoperability across A2A, ACP, MCP and UHP.
Open-source optimization and quality layer/framework for AI agents. It builds and runs agents from one spec, compiles across eight frameworks, supports evaluation-first release decisions, Agent Quality Records, GEPA optimization and A2A interoperability.
Open-source quantum-and-agentic-AI SDK/research platform with a unified API, backend switching and pre-built quantum machine-learning and optimization algorithms across PennyLane, Qiskit, Cirq, Amazon Braket, TKET and D-Wave Ocean.
Protocol-first AI-agent framework built on DSPy, with MCP and A2A support and automatic connection management.
Market Position
Superagentic AI positions itself as a full-stack, open-source agent-engineering company rather than a foundation-model developer or generic SaaS vendor. SuperQode overlaps with coding-agent and harness ecosystems such as Codex, Claude Code, GitHub Copilot and other ACP/A2A-connected tools, while SuperOptiX overlaps with agent frameworks and optimization ecosystems such as DSPy, LangChain and CrewAI; its differentiation is the combination of interoperability, evaluation-first governance, optimization, durable execution and agent-focused experience.
Leadership
Founders
Shashi Jagtap
Founder and CEO; nearly 20 years in IT, including more than six years at Apple working on Xcode automation, XCTest and continuous-integration infrastructure. He founded XCTEQ in 2018 and previously built mobile DevOps and test-automation tooling.
Executive Team
Shashi Jagtap
Founder & CEO
Former Apple engineer in the Xcode organization; worked on Xcode automation, XCTest and CI infrastructure. Founder of XCTEQ (2018) and organizer of London/San Francisco agent-engineering communities.
Founding Story
After leaving Apple in April 2025, Shashi Jagtap incorporated Superagentic AI on April 28, 2025. He started it as a lean, initially one-person company to carry his Developer Experience experience into Agent Experience: building tools and frameworks that make agents useful, dependable, scalable, safe and accountable rather than pursuing AGI or replacing human oversight.
Business Model
Revenue Model
Open-source products are offered free for self-hosting/use; revenue is planned or generated through paid design-partner engagements, one-off repository benchmarking and scorecards, and recurring measurement/optimization services delivered in the company's cloud or self-hosted for customers.
Pricing Tiers
Open-source SuperQode terminal and optimizers, including the Harness Hub and hosted A2A agent.
Single engagement to benchmark candidates on a customer's repository and deliver a scorecard, reproducible manifest and written recommendation.
Ongoing spend measurement and reruns when a model or specification changes, in Superagentic's cloud or self-hosted.
Target Markets
- Individual developers and agent architects
- Infrastructure builders and engineering teams
- Technology teams and startups
- Enterprise engineering, research and innovation teams
- Researchers working on agentic and quantum AI
- Building and governing coding-agent workflows across repositories
- Selecting, benchmarking and optimizing coding-agent harnesses
- Developing production-grade, multi-agent systems
- Agent evaluation, observability, release governance and quality assurance
- Agentic DevOps and software delivery
- Quantum machine learning and quantum optimization experimentation