From Co-Pilots to Autonomous Engineering: The Rise of Agentic AI in Software Delivery
The GenAI revolution is evolving. Where once the conversation circled around AI “co-pilots”—helpful assistants embedded in coding, testing, or documentation—we’re now entering a new phase: the age of Agentic AI. At Nallas, we believe this shift will fundamentally redefine how software is engineered, delivered, and governed.
This blog outlines why Agentic AI matters, how it’s different from current tools, and how enterprises can prepare to integrate it into real-world delivery.
Co-Pilot Era: Useful, But Incomplete
Over the last 24 months, GenAI co-pilots have gained traction:
- GitHub Copilot helps developers autocomplete boilerplate code.
- OpenAI ChatGPT assists product managers in drafting requirements.
- Anthropic Claude and Google Gemini summarize user feedback or legacy documentation.
But these tools are reactive. They assist, but they don’t act.
At Nallas, our experience deploying AI across 50+ SDLC environments made one thing clear: co-pilots improve productivity, but they don’t orchestrate outcomes.
What Is Agentic AI?
Agentic AI refers to autonomous AI systems capable of executing multi-step goals with minimal human input. These agents:
- Plan tasks based on objectives
- Act by invoking tools, APIs, and scripts
- Learn from feedback loops
- Adapt to new conditions and priorities
Unlike co-pilots that wait for prompts, agents initiate actions, handle branching workflows, and update their own plans. They’re not just assistants—they’re teammates (Stanford HAI).
Where Agentic AI Fits in the SDLC
SDLC Phase | Traditional AI Tools | Agentic AI Use Case |
Requirements | Auto-generate stories from prompts | Continuously refine backlog by watching stakeholder tickets |
Design | Suggest wireframes or diagrams | Auto-create architecture proposals and get them reviewed |
Development | Code suggestion via Copilot | Multi-repo code generation and testing across stacks |
Testing | Generate test cases from stories | Create, run, and optimize entire test suites |
Release Management | Notify teams about CI/CD pipelines | Trigger deployments, rollback on error, notify stakeholders |
Documentation | Summarize files | Maintain evolving documentation across the lifecycle |
Agentic AI brings agency—the ability to decide and act—into every corner of the SDLC.
A Real-World Example: Legacy Modernization with Agentic AI
One widely referenced example comes from Microsoft’s use of AI to modernize legacy systems. Microsoft applied large language models like GPT-3 to refactor legacy code, enabling:
- Automated code translation from VBScript to Python
- Semantic code search across old and undocumented systems
- Integration of auto-generated test cases for converted modules
Result:
- Reduced modernization timelines from months to weeks
- Significantly improved test coverage with minimal manual intervention
- Demonstrated scalability for multi-million-line codebases
Why This Matters Now
- Rising Complexity: Modern apps span microservices, APIs, infra-as-code, and user-facing layers—too complex for prompt-based tools.
- Talent Scarcity: Agentic AI scales senior engineering capacity without scaling headcount (McKinsey: The State of AI in 2024).
- Governance Demands: Agents can embed guardrails—e.g., enforce SOC2 compliance during CI/CD (Deloitte on AI Governance).
Agentic AI is not a productivity tool. It’s an engineering operating model.
How Nallas Enables Agentic AI for Clients
We’ve built foundational capabilities across:
- Autonomous DevOps Pipelines: Agents that manage release workflows, environment prep, and rollback triggers.
- Test Intelligence: LLM-based agents that create, evaluate, and tune regression test coverage.
- Architecture Generation: GenAI that reads business goals and outputs design blueprints in minutes.
- Knowledge Agents: Persistent memory agents that evolve design docs, code summaries, and wiki pages.
Every agent is grounded in client-specific context using Retrieval-Augmented Generation (RAG), usage logs, codebase embeddings, and live JIRA states
Getting Started with Agentic AI: A 3-Step Maturity Path
Stage 1: Co-Pilot Adoption
- Use assistants for code, test, and document generation
- Focus on productivity boosts
Stage 2: Task Agents
- Automate well-defined workflows (e.g., test case execution, CI checks)
- Introduce feedback loops
Stage 3: Autonomous Agents
- Delegate complex, cross-functional goals (e.g., legacy modernization, full test suite optimization)
- Monitor agent decisions and refine policies
The Future: Agentic Platforms
By 2026, leading engineering teams will run on agentic platforms—systems where:
- Business goals trigger end-to-end workflows
- AI agents collaborate with humans across tools
- Every decision is explainable, traceable, and improvable
We’re building toward that future—today.
Author
Karthik Subramanian
VP of Strategy
