The Future of Programming is Natural Language
Successive generations of programming languages—from bits and ones, through assemblers and low-level languages like C, to object-oriented and functional paradigms — have become progressively easier for humans to learn and reason about.
In the age of AI, it seems increasingly clear that the “languages” of the future will be English (or natural language more broadly): carefully designed prompts, enriched with contextual data from vector databases and structured sources.
English for Goals, DSLs for Precision, Code for Guarantees#
In such a world, software specification and implementation begin to converge: requirements, constraints, tests, and documentation become executable intent. Prompts act as the high-level interface, while models orchestrate code generation, data transformation, and tool invocation.
Domain-specific guardrails, evaluation suites, and provenance tracking will be as essential as compilers and type systems were in previous eras. Hybrid stacks will persist—natural language for intent, DSLs for precision, and traditional code where performance, determinism, or safety is paramount. But the center of gravity shifts: we “program” by expressing goals in English, grounded by context and verified by automated feedback loops.
Paradigm Shifts, Stable Methodologies#
These shifts in programming paradigms do not inherently require changing software development methodologies. Teams can continue to rely on well-established practices and toolchains—such as Scrum, Kanban, version control, CI/CD (Continuous Integration and Continuous Deployment/Delivery), automated testing, and DevOps—regardless of whether they adopt object-oriented, functional, reactive, or data-oriented approaches.
These methodologies focus on how work is planned, executed, and delivered, while programming paradigms focus on how code is structured and reasoned about. As a result, paradigm shifts can be accommodated within existing processes by adjusting coding standards, review practices, architectural guidelines, and quality gates, without discarding proven ways of organizing and delivering work.
The Solution#
Therefore, we decided to create hal.guru - a platform for building AI agents designed to meet the challenges of the future. It empowers teams to prototype, deploy, and scale intelligent agents with reliability and speed, bridging the gap between cutting-edge research and real-world applications.
| Last updated: | 2025-09-26 |