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🧠 The AI Engineering Frontier: Spec-Driven Development, SWE-Bench, and Modular Agents
The past month inside CoreStory’s engineering slack channels have been a masterclass in the evolution of AI coding assistants — from copilots to fully agentic collaborators. Here’s what caught our attention, what we built, and what’s shaping the broader industry conversation.
🚀 CoreStory × GitHub Copilot: The Future of Spec-Driven Development
At GitHub Universe 2025, CoreStory engineers demonstrated a major milestone:
GitHub Copilot + CoreStory rewrote an enterprise-grade feature with full test coverage and 80–90 percent parity in a single session.
The process followed a new spec-driven development loop:
- Pull a ticket from backlog
- Check the spec for existing behavior
- Align the code with the spec
- Draft integration and BDD tests
- Generate code to satisfy tests
- Run and iterate until all tests pass
This isn’t just prompt-engineering — it’s AI engineering with structured context. As GitHub itself explores the role of structured intent in Copilot’s evolution, CoreStory’s approach shows how specs can act as a control plane for AI development.
→ Explore GitHub’s latest reflections on AI copilots: GitHub Blog
→ Take a look at CoreStory's Talk: Smarter by design: Spec-driven development with CoreStory and GitHub Copilot
🧩 Benchmarking Agents Against SWE-Bench
Following the demo, engineers explored integrating CoreStory with SWE-Bench, the open benchmark for code-level reasoning.
- Challenge: each SWE-Bench task originates from a unique “base commit,” making ingestion of all repositories computationally expensive (≈ 88 million LOC).
- Proposal: define a “hard-mode” subset of the 45 most complex tasks to prove real-world performance gains using structured specs.
The goal? Demonstrate that AI agents augmented by CoreStory’s code intelligence outperform base models by an order of magnitude on hard tasks — the kind real developers actually face.
🧠 From MCP to Skills: Modular, Composable Agents
Founder Anand Kulkarni spotlighted a pivotal industry shift away from the Model Context Protocol (MCP) toward lightweight, skill-based architectures.
- Simon Willison’s write-up and Anthropic’s Skills announcement both capture this movement.
- GTM Engineering Director, John Bender, extended it by embedding a Claude Skill File directly into CoreStory’s Test Generation Playbook — effectively creating modular skill packs that enhance test coverage, reasoning, and spec alignment.
For more on the trend, see Factory.AI’s “Code Droid” technical report, which outlines how composable AI components are reshaping developer workflows.
🔁 Memory, DSPy, and Long-Context Agents
CTO Charath Ranganathan shared a deep-dive from creator Avishek (AVB) on memory models and DSPy pipelines — vital for building agents that can think across sessions.
- Watch the video for a practical guide to agentic memory.
- Related research: Claude Code on the Web and Stanford DSPy, both exploring how structured context expands reasoning limits.
The conversation signals a broader shift in AI systems: from stateless completion engines to context-aware collaborators.
🌐 Industry Spotlight: Agentic AI Momentum
Across the AI ecosystem, the same theme keeps surfacing — autonomy with accountability.
- Second Thoughts: - GPT-5 and the Case of the Missing Agent — an analysis of orchestration vs. autonomy in LLM design.
- ArXiv 2510.18212 — new research on large-scale evaluation of multi-agent collaboration.
Together, these mark a turning point: AI development is moving from “autocomplete” toward “autonomous co-creation.”
🧭 Looking Ahead
As CoreStory continues to push the boundaries of Spec-Driven Development, our engineering team is:
- Building Claude-based test-generation agents with skill modularity
- Expanding SWE-Bench evaluations for enterprise-scale scenarios
- Publishing learnings from our GitHub Universe demo
Stay tuned — this is just the beginning of code intelligence as infrastructure.
Follow CoreStory on LinkedIn and Twitter for ongoing insights into AI-powered software engineering.
