Artificial Intelligence Software Agent
Roadmap Creators
Artificial Intelligence (AI) Software Agent Roadmap
- 2AISA - Artificial Intelligence (AI) Software Agent
The technology we selected is an AI Software Agent capable of receiving natural language prompts, generating complex software development strategies, executing all programming tasks to build end-to-end software applications, and deploying those applications for business or leisure purposes. This is a Level 2 Technology Roadmap. Level 1 encapsulates the ecosystem of all AI System Technologies, while Levels 3 and 4 would include foundational technologies central to the form and function of AI, including but not limited to Machine Learning, Neural Networks, Large Language Models, and Graphics Processing Units (GPUs).
Roadmap Overview
Since ChatGPT-4 was released in 2022, it has amazed people worldwide and marked the beginning of a new era in AI innovation. The prosperity has expanded from hardware advancements like GPUs to breakthroughs in large language models (LLM), natural language processing (NLP), and machine learning (ML). This year, 2024, has been nominated the year of AI agents due to the rapid advancements in AI technologies, with emerging trends such as multi-agent systems and agentic AI. These agents are reshaping industries by automating processes, enhancing productivity, and enabling more multi-model interactions. Software AI agents are becoming increasingly sophisticated, offering new possibilities for automation, decision support, and human-AI collaboration across various domains.
AI agents possess several core capabilities:
- Perception: gather data and documents from database, APIs
- Reasoning: analyze data, identify patterns, and make informed decision using advanced algorithms and machine learning
- Action: autonomously perform tasks, from answering queries to executing complex processes
- Learning: continuously learn from experience and improve performance over time
According to the AI Benchmarking Report by CodeSignal, while AI agents are increasingly powerful, the creativity and intuition of human engineers demonstrate when dealing with complex or cutting-edge problems still marks a weakness of AI agents. This technology roadmap explores the potential of AI software agent in automation, decision making, and human-AI collaborations.
Reference:
- https://www.leewayhertz.com/ai-agents/
- https://yellow.ai/blog/ai-agents/
- https://codesignal.com/blog/engineering/ai-coding-benchmark-with-human-comparison/
Design Structure Matrix (DSM) Allocation
Roadmap Model using OPM
The Object-Process-Model (OPM) of the 2AISA AI Software Agent is provided in the figure below. This diagram captures the main object of the roadmap, its various processes and instrument objects, and its characterization of two relevant Figures of Merit (FOMs): Productivity and Accuracy.
Figures of Merit (FOM)
Alignment with “Company” Strategic Drivers: FOM Targets
Our “hypothetical” company provides software AI agents to help facilitate the software development process for individual users and business clients. The following strategic drivers are essential for ensuring our product meets market needs and stays competitive.
The first and second drivers align closely with our technology roadmap, as the AI industry is still in the early, rapid-growth phase of the S-curve. In this stage, innovation cycles are short, and all companies are focused on advancing product performance and expanding the market. The third driver, however, will become increasingly important as the market approaches saturation and companies shift their focus from pure technology advancement to competing for market share through added-value features and services around the core technology.
| # | Strategic Driver | Alignment and Targets |
|---|---|---|
| 1 | To create value for our users by increasing their productivity and reducing development time at a reasonable price | The 2AISA technology roadmap prioritizes the productivity and cost-effectiveness of the software AI agent as its primary FOMs. The goal is to enhance productivity by 20% while achieving a 20% cost reduction per project. |
| 2 | To ensure the quality and accuracy of the generated codes that our users can trust and rely on | The 2AISA technology roadmap will continually advance in achieving 95% completion accuracy and reduce the number of violations. |
| 3 | To deliver a seamless user experience with robust compatibility for business clients, ensuring smooth integration with other systems. | The 2AISA technology roadmap currently does not prioritize user application enhancement because we want to build a solid foundation before expanding into user-centric features. |
Position of Company vs. Competition: FOM Charts
These FOM Charts obtained at ArtificialAnalysis.ai, begin to depict the competitive landscape of the leading AI Companies developing emerging AI Agents with profound coding and software engineering capabilities. The figures above depict the 14 of the leading AI Companies and their performance on two key FOMs: Code Quality against the Humaneval Benchmark which consists of 164 complex coding tasks, and price.
Tradespace
The visualization above is modeled in Python, and begins to display some initial trade space analysis of the same 14 leading AI Companies discussed above in the FOM Charts. This trade space view begins to consider the companies that emerge on the Pareto Frontier, or in other words, those leading the charge in the race to develop the "best" AI Software Agents as they perform against specific coding benchmark tasks and cost for users. The utopian point, represented by the star in the upper left corner of each graph is considered the optimal position in which users of the AI Agent get 100% code quality for no cost.
Technical Model: Morphological Matrix
This morphological matrix maps out five critical decision variables (Quality, Cost, Speed, Latency, and Model Size) across 14 different LLM models, revealing key trade-offs in the current AI landscape. The matrix illustrates a clear performance-resource trade-off pattern: models with higher parameter counts (like o1 Preview at 750B and o1 Mini at 500B) generally achieve superior quality scores (0.95 and 0.93 respectively) but at significantly higher costs ($75 and $15 per 1M tokens) and increased latency (31.49s and 15.32s). Conversely, smaller models like Gemini 1.5 Flash and GPT-4o Mini demonstrate impressive efficiency with lower latency (0.38s and 0.45s) and costs ($0.37 and $0.76 per 1M tokens), while maintaining respectable quality scores (0.84 and 0.86).
Key Publications and Patents
Publications
| Publication Name | Function |
|---|---|
| The landscape of emerging AI agent architectures for reasoning, planning, and tool calling: A survey | This survey paper offers an important analysis of the current capabilities and limitations of AI agent architectures in achieving complex goals requiring reasoning, planning, and tool calling. The paper is particularly relevant because it provides a taxonomy of single and multi-agent architectures, examines key patterns and divergences in their design choices, and evaluates their impact on accomplishing goals. For a roadmap focused on AI agents for software development, the paper is crucial because it helps to understand which architecture may be best suited for specific software development tasks, the role of leadership in agentic systems, agent communication styles, and critical planning, execution, and reflection phases. (The Landscape of Emerging AI Agent Architectures for Reasoning, 2024) |
| The future of software engineering in an AI-driven world | This paper presents a vision for the future of software development where AI, particularly LLMs, plays a pivotal role in enhancing developer productivity. The paper is important for a roadmap focused on AI agents for software development because it outlines key research challenges and opportunities in this evolving landscape. Specifically, the paper highlights challenges in areas such as requirements elicitation, software design, development and testing, and software maintenance within an AI-driven framework. It underscores the need for explainable AI in design, automated test case generation with effective oracles, and the importance of continuous background maintenance by AI agents. (Valerio Terragni, 2018) |
| Future of artificial intelligence in agile software development | This research paper emphasizes the transformative potential of AI, including LLMs, generative AI models, and AI agents, in revolutionizing software project development, particularly in the context of Agile methodologies. The paper is significant because it proposes an approach to leverage AI tools and technologies to enhance various stages of the Agile software development process. For a roadmap focused on AI agents for software development, this paper is valuable as it proposes the integration of AI in Agile processes like Extreme Programming (XP) and Lean Software Development (LSD), outlining AI's potential for automated testing, debugging, risk assessment, continuous integration, deployment, process optimization, performance monitoring, and quality assurance. (Mariyam Mahboob, 2024) |
Patents
| Patent Name | Function |
|---|---|
| Intelligent software agent to facilitate software development and operations | This patent proposes an intelligent software agent platform that monitors software CI/CD pipelines, extracts log data, applies machine learning models, and generates pipeline health check analysis reports. This patent is relevant for a roadmap focused on AI agents in software development as it provides a concrete example of how AI agents can be implemented to automate quality assurance and improve the efficiency of software development processes. The patent highlights the technical advantages of using AI agents for real-time monitoring, prediction of future pipeline operation, and automated reporting, contributing to a more streamlined and robust software development process. (USA Patent No. US11481209B2, 2022) |
| Executing artificial intelligence agents in an operating environment | This patent details a system and method for executing AI agents within an operating environment, specifically focusing on aspects such as workflow management, model training, and performance monitoring. While not directly focused on software development, this patent provides valuable insights for a roadmap on AI agents in software development by illustrating how AI agents can be orchestrated and managed within a broader system context. The patent's emphasis on workflow editor interfaces, model deployment, and performance monitoring can be extrapolated to guide the design and implementation of AI agents in software development pipelines. (World Intellectual Property Organization (WIPO) Patent No. WO 2021/084510 A1, 2021) |
Financial Model
Assumptions:
1) Our revenue growth is projected to align with our user growth. Thus revenue and operation cost will grow at a rate of 30% YOY 2) We do not trained our own LLM, but sent API request and recieve from GPT-4o API, the input token price is $2.5 per 1M tokens; output price is $10 per 1M tokens 3) Benchmarking companies in growth stage, we invest 30% of out revenue into R&D expense to maintain competitive edge 4) Benchmarking leading AI agent company Anthropic's 2024 projected revenue of 200M as our initial revenue 5) Annual discount rate 8%
Similar R&D Projects
| Project Name | Start Date | Function | TRL Level |
|---|---|---|---|
| Devin AI | 2024 | An AI-powered software engineer designed to streamline software development. It assists developers by generating code, solving problems within development environments, and improving overall efficiency in software engineering tasks. | 7–8 (System prototype nearing or completed; ready for operational environment) |
| Replit Agent | 2024 | An advanced AI agent tailored to assist in building software projects from start to finish. It comprehends natural language prompts to create applications, configures development environments, installs dependencies, executes code, and deploys applications seamlessly in real-world settings. | 7–8 (Prototype tested in real environments with operational capabilities) |
| Programmer's Apprentice | 1987 | This pioneering project applied artificial intelligence to automate the programming process. It explored programming as a domain for understanding knowledge representation and reasoning, developed a theoretical framework for expert programmer behavior, and provided automated support for software processes spanning requirements acquisition to implementation.Source. |
3–4 (Concept proven, but requires further experimental and theoretical development) |
| SWE-agent | 2024 | A cutting-edge system that integrates language models into software engineering workflows. It autonomously identifies and fixes bugs, addresses issues in GitHub repositories, and introduces an Agent-Computer Interface (ACI) to enhance interactions between the agent and its operational environment.Source. |
6–7 (Demonstrated in relevant or operational environments) |
Technology Strategy Statement and Swoop
Bibliography
- Gabriel Duford, J.-F. A. (2021). World Intellectual Property Organization (WIPO) Patent No. WO 2021/084510 A1.
- Mariyam Mahboob, M. R. (2024). Future of Artificial Intelligence in Agile Software Development. International Journal of Development Research.
- MarketsandMarkets. (n.d.). Retrieved from https://www.prnewswire.com/news-releases/ai-agents-market-worth-47-1-billion-by-2030---exclusive-report-by-marketsandmarkets-302246356.html
- Renoi Thomas, S. V. (2022). USA Patent No. US11481209B2.
- The Landscape of Emerging AI Agent Architectures for Reasoning, P. a. (2024, April 17). Tula Masterman, Sandi Besen, Mason Sawtell, Alex Chao. Retrieved from https://arxiv.org/: https://doi.org/10.48550/arXiv.2404.11584
- Valerio Terragni, P. R. (2018). The Future of Software Engineering in an AI-Driven World. In Proceedings of International Workshop on Software Engineering in 2030. ACM.