Responsible Software Development with an Agentic AI Workforce

Software development has, for a long time, relied on manual effort and isolated toolchains to build products. We have slowly introduced automation to handle repetitive tasks in our continuous integration (CI) pipelines, and we have used code generation tools like ReSharper and CodeRush to speed up repetitive tasks in coding. However, as AI evolves, we can get tools to help create small POCs from prompting, and we can now imagine a future where “agentic AI” systems play a key role in our software development lifecycle (SDLC). These agentic systems will generate more than simple functionality. It will be able to coordinate tasks, perform security checks, communicate cross-functionally, and dynamically adapt to complex requirements.

However, this shift isn’t going to come from a single “silver bullet” prompting that will create perfect applications. Instead, AI will be included in planning and executing software deliveries. It will follow our structured processes and take on roles in our software teams; it can take on a system-thinking mindset, where we can ensure the human side of development remains central while leveraging agentic AI’s computational strengths. Below, we explore how to set up teams and roles for an AI-empowered workforce and draw parallels from ongoing research — including the “communicative agents for software development” model introduced by ChatDev Paper — to show how software architects, developers, product managers, and security professionals might collaborate with these autonomous AI agents.

1. Revisiting System Thinking

“Organizations which design systems are constrained to produce designs which are copies of the communication structures of these organizations.”

— Melvin Conway (Conway’s Law)

In a recent Medium article, I discussed how system thinking allows us to see software not just as technology but as socio-technical systems — converging code, organizational structures, and human interaction. System thinking reminds us that if we change the “who” or the “how” of our development, we inevitably change the software itself.

When we factor in agentic AI — software agents capable of planning, decision-making, and self-organization — we must consider how those agents fit into our organizational structure. If we apply Conway’s Law too narrowly, we might bolt AIs onto existing teams without adjusting processes or communication paths, leading to friction. Instead, applying system thinking lets us craft an architecture that balances AI autonomy with the human “command and control” model.

2. Embracing an Agentic AI Workforce

Agentic AI can be seen as the next evolutionary step beyond chatbots or code-completion tools. These AI systems take on roles, not just tasks, and they can:

Plan and Goal-Set: Break high-level objectives into smaller, manageable milestones.
Coordinate: Communicate with other AI agents or human colleagues to sequence tasks effectively.
Adapt and Learn: Observe outcomes (e.g., from test failures or user feedback) and autonomously refine system components.
Act Autonomously: Execute multi-step tasks, such as coding a new microservice, setting up CI/CD pipelines, verifying compliance with security policies, or rewriting design specs based on real-time changes.

However, responsibility must remain at the forefront. Amy Edmondson’s research on Psychological Safety teaches us that mistakes will spiral if team members (including AI “members”) operate without accountability or open communication. Ensuring we have the right roles and “checks and balances” is key.

3. From Single AI Queries to Collaborative AI Agents

Past experiments with GPT-based tools — like GPT-Engineer or code-generation approaches — often rely on a single agent that receives a prompt and spits out code. While these can be quick wins, they are prone to errors and lack depth in design, review, and testing.

Recent research, such as the ChatDev Paper on “Communicative Agents for Software Development,” underscores an alternative: multi-agent collaboration following the classic waterfall or iterative SDLC phases — Requirements, Design, Coding, and Testing — managed by specialized agents. An example is ChatDev, which uses:

A “CEO” or “Requirements Analyst” Agent: Gathers and clarifies user stories and acceptance criteria.
A “CTO” or “Architect” Agent: Plans system design, domain models, threat models, and high-level components.
A “Programmer” Agent: Writes and refines actual code, responds to in-depth comments, and implements TDD.
A “Reviewer” Agent: Conducts code reviews and checks for compliance with best practices.
A “Tester” Agent: Executes static and dynamic tests, integrating with the build pipeline to confirm code correctness and security baseline adherence.

Through multi-round dialogues in natural language and code-level feedback, these agents refine and converge on workable solutions while explaining logic to one another. Crucially, their collaboration must integrate seamlessly with established processes and tools — like Azure DevOps or Jira — to document conversations, track changes, and give stakeholders full transparency.

4. Structuring Teams and Agentic Roles

A thriving AI-empowered software development environment hinges on having clear roles for humans and AI agents and well-defined collaboration patterns. Alongside design and coding specialties, ensuring compliance, security requirements, verifiability, and human-centric usability is paramount. Below is a potential structure:

1. Technical Writer (Documentation & Requirements)

Role: Drafts the user stories, acceptance criteria, usage docs, and compliance references (e.g., HIPAA for healthcare, GDPR for data privacy).
AI Collaboration: The “Technical Writer” agent reviews meeting transcripts and user requests, then clarifies ambiguous sections with the “Architect” agent. It can also cross-check requirement statements against known regulations or internal policy libraries.
Human Touch: Real product managers and compliance officers sign off on final wording, ensuring that the user stories and documentation align with regulatory standards and business outcomes.

2. Developer (Feature Implementation)

Role: Implements features by following design specs and writing code while ensuring security requirements (e.g., input validation, encryption modules) are integrated.
AI Collaboration: The “Developer” agent consults the “Security” agent for guidance on applying security best practices, such as privileged accounts or standard authorization flows. The AI developer can also run local tests and code scans for known vulnerabilities.
Human Touch: Senior developers oversee critical modules — like billing — to confirm the correctness of advanced business logic.

3. Security Specialist (Security & Compliance Integration)

Role: Identifies threats, proposes mitigations, ensures Zero Trust principles, incorporates compliance checks, and consults on secure coding.
AI Collaboration: The “Security” agent scans code for known vulnerabilities — like SQL injection or unsafe dependencies — using a knowledge base updated with relevant frameworks (e.g., OWASP Top 10).
It can also parse compliance guidelines (e.g., ISO 27001, PCI-DSS) and compare them against system logs, code comments, or configuration files, flagging areas where the system may fall short.
Human Touch: A Security Black Belt reviews final threat models and cross-verifies them with real-world security contexts or specialized penetration testing tools.

Bruce Schneier says, “Security is a process, not a product.” This process requires continuous human feedback loops for critical sign-offs.

4. Compliance & Audit Agent (Ensuring Regulatory Standards)

Role: Focuses on verifying that code changes, data usage, and deployment practices align with relevant regulations or internal governance rules.
AI Collaboration: The “Compliance” agent can run an automated checklist. For example, it might confirm that personal data fields are anonymized or that a WAF protects endpoints.
It can also periodically request evidence from the “Developer” agent or “Security” agent, e.g., “Show me proof you’re hashing passwords with SHA-256 or better.”
Human Touch: Auditors or legal teams validate edge cases and sign off on official reports.
Regulatory standards often evolve, so humans must ensure the AI’s internal knowledge base is continuously updated with the latest legal precedents.

5. Software Architect (System & Infrastructure)

Role: Oversees the domain model, architectural patterns (e.g., microservices vs. monolith), database choices, and deployment topology.
AI Collaboration: The “Architect” agent automates design diagrams and checks for scalability, reliability, and integration with existing solutions while referencing compliance constraints (e.g., data locality for GDPR).
It interacts with the “Security” agent to ensure that new architectural proposals, such as event-driven microservices, follow encryption-in-transit policies.
Human Touch: Architecture decisions must weigh cost, vendor lock-in, or organizational strategy. This includes understanding the business context that AI might not fully grasp.

6. Product Manager (Backlog Management & Stakeholder Alignment)

Role: Ensures all stories and features align with business goals, prioritizes items, and fosters feedback loops with real users.
AI Collaboration: The “Product Manager” agent can forecast timelines based on velocity data and user feedback from test releases and ensure that security or compliance items remain high-priority in the backlog.
Human Touch: True product sense — deciding the next strategic features — still requires a deeply human perspective. AI can make suggestions, but humans must confirm the final roadmap.

7. Test Engineer (Quality Assurance & Validation)

Role: Focuses on acceptance tests, integration tests, regression suites, and verifying that security requirements and compliance constraints work as expected.
AI Collaboration: The “Test Engineer” agent runs automated scans to confirm that security configurations are enforced. For instance, it verifies that user tokens expire properly or that logs mask sensitive data.
It orchestrates end-to-end tests, comparing results against compliance benchmarks.
Human Touch: Engineers still run manual testing for usability, performance, or stress scenarios. This ensures that subjective feedback — like UI clarity or multi-regional performance — isn’t overlooked.

8. UX/CX Designer (User & Customer Experience)

Role: Ensures human-centered interfaces, performs user research and validates that people understand the system’s purpose and interaction design. This includes “features discovery” — progressive user testing in small increments — to gather early feedback on layout and flow.
AI Collaboration: A “UX/CX Designer” agent can compile user feedback data, simulate user journeys, and verify that the final product matches the Product Manager’s approval.
The AI might compare design mockups or storyboards against the deployed interface, flagging discrepancies or missing features.
Human Touch: True empathy and insight into user needs still require face-to-face testing, interviews, and iterative design sessions. To ensure consistent brand identity and clarity, UX designers must confirm final product screens and user flows.

5. Coordinating Phases and Multi-Agent Collaboration

5.1 Design: Natural-Language Discussions

During the design phase, natural language dialogues dominate. For instance, the “Architect” agent asks the “Security Specialist” about threat vectors, while the “Compliance & Audit” agent references specific regulatory texts. Mutual understanding in plain language ensures that all stakeholders (technical and non-technical) grasp the design rationale, reducing confusion later.

5.2 Coding: Code-Centric Reviews and Security Checks

In the coding phase, agents switch to more code-oriented communication. A “Developer” agent might produce a scaffold, and the “Security” agent or “Reviewer” agent can prompt them to add stricter validation. Meanwhile, the “Compliance” agent may scan the code to ensure appropriate anonymization or encryption calls are present where needed. Focusing on correctness and compliance makes the coding process more robust.

Throughout these phases, established processes and tools such as Azure DevOps or Jira can serve as the persistent backbone for recording detailed conversations, linking tasks to compliance checks, and ensuring full transparency for all stakeholders.

5.3 Testing: Automated Static and Dynamic Verifications

In test phases — both static code analysis (code review) and dynamic testing — the “Test Engineer” and “Security” agents rely on continuous feedback to finalize the solution. Each agent can parse compiler errors or runtime exceptions, automatically feed them into relevant policy checks, and ensure that any non-compliance or security flaw is flagged and corrected. The cycle ends only when compliance thresholds and security baselines are met.

6. Governance, Security, and Ethical Oversight

When we say “autonomous” or “agentic,” it doesn’t mean “unaccountable.” Much like how DevSecOps merges automated checks with human oversight, an agentic AI workforce requires robust governance. Here are some practical strategies:

Sandboxing: Ensure AI-driven changes are tested in secure, isolated environments before production.
Guardrails & Observability: Implement logging, version control, and an “audit mode” that captures the AI’s reasoning steps, especially around data handling and cryptography.
Compliance Verification: Build automated checks for regulatory items (e.g., HIPAA, PCI-DSS) directly into the pipeline.
Controlled Autonomy: Restrict what the AI can do without human approval — particularly for database schema changes or sensitive data migrations.
Privacy & Data Handling: If data is used to train or inform AI, verify compliance with GDPR-like regulations, and apply data minimization or anonymization practices.

7. Toward Post-SaaS Agility

We often hear we’re at the “end of SaaS,” with organizations building modular, AI-driven solutions that combine internal code, open source, and specialized microservices. Agentic AI might not replace SaaS entirely but can function as an adaptable layer orchestrating many external and internal services in real time. The outcome is a dynamic, “composable” architecture — where each piece (including the AI workforce) can evolve independently.

“The notion that SaaS is dying might be exaggerated. It’s more accurate to say SaaS is evolving to be more agile, integrated, and driven by intelligent orchestration.”

— Adapted from multiple end-of-SaaS articles

The lines between “in-house development” and “SaaS integration” in this new environment blur. The AI workforce acts as a continuous integrator, ensuring teams remain nimble enough to adopt or decommission services based on real-time performance metrics or user demands.

8 Conclusion: Building a Responsible Future

Responsible software development with an agentic AI workforce rests on a few key pillars:

System Thinking: Recognize the interplay between human roles, AI agents, compliance requirements, and organizational structures.
Role Definition: Just as humans have roles, so do AI agents — Technical Writer, Developer, Security Specialist, Compliance & Audit, Architect, Product Manager, Tester — ensuring domain-specific checks and balances.
Multi-Agent Collaboration: Move beyond single-shot code generation. Layer conversations in natural language for design and in code for security, referencing approaches like ChatDev Paper to continuously refine.
Governance, Privacy, and Security: Maintain visibility into agent decisions, adopt a Zero Trust approach for code merges, incorporate compliance checks, and ensure data protection from the earliest phases of development.
Agile Integration & Tooling: Architect solutions for continuous evolution — so teams can adapt as demands shift and new agentic tools emerge. Embrace established platforms (e.g., Azure DevOps, Jira) to document every interaction, resulting in a transparent, auditable chain of events for all stakeholders.

Much like learning to ride a bike, the process begins awkwardly and might involve scrapes along the way. Each failure is a chance to refine processes, roles, or communications. Over time, the synergy between human ingenuity and AI’s dynamic capabilities will steer us toward more robust, scalable, and ethically grounded software applications.

The “end of SaaS” conversation is less about discarding SaaS altogether and more about harnessing these agentic models to create agile, scalable sub-systems that serve evolving human needs. Through balanced roles, clear boundaries, and strong feedback loops, we can ensure that we don’t just build faster with agentic AI — we build responsibly.

Call to Action

Start Small: Experiment with an AI “Programmer” or “Security” agent in your DevOps pipeline. Observe performance in reviewing PRs or flagging vulnerabilities.
Apply Multi-Agent Patterns: Borrow ideas from the ChatDev paper and define your “Designer,” “Coder,” “Security,” and “Compliance” agents for a small side project.
Embed Security & Compliance Early: Involve “Security Specialist” and “Compliance & Audit” agents from day one, not merely as afterthoughts.
Share Feedback & Learn: Document friction points when introducing these AI roles using your established tools and processes — like Azure DevOps or Jira — to maintain full transparency and clear traceability.

By embracing these steps and coupling them with strong organizational alignment, we can collectively envision a future where AI-empowered teams and humans build the next wave of secure, compliant, and impactful software together.

References & Further Reading

“Complexity often signifies confusion, yet system thinking provides a lens for clarity.” — From my Medium article on System Thinking

By recognizing the bigger picture and balancing the strengths of AI and humans, we can shape a future where software development becomes more agile, responsible, and ultimately more human-centered — even with AI as a co-creator of our technical processes.