The Secret to Managing AI Coding Agents: Think Like a Tech Lead, Not a Coder
How structured documentation and strategic chunking can turn chaotic AI assistance into a productivity powerhouse
After spending countless hours in the AI-assisted coding trenches during a major website relaunch, I’ve discovered something counterintuitive: The best way to work with AI coding tools isn’t to code alongside them—it’s to manage them like you would a team of brilliant but easily distracted junior developers.
Here’s the framework that’s transformed my development workflow from AI-augmented chaos to streamlined productivity.
Table of contents
The Multi-Agent Orchestra Approach
Instead of relying on a single AI tool for everything (a common rookie mistake), I’ve adopted what I call the “specialist delegation” model. Each phase of development gets its own expert:
- Business Analysis Phase: Deploy consumer-grade models like ChatGPT Pro or Gemini Pro ($20/month tier) to transform stakeholder interviews into properly formatted user stories. These models excel at pattern recognition and standardization—perfect for the fuzzy front end of requirements gathering.
- Architecture Design Phase: This is where you bring in the heavy artillery. Models like ChatGPT Pro’s o3 or Claude Opus v4 become your senior architects, capable of sophisticated system design discussions. Feed them your existing codebase, tech stack constraints, and business requirements, then engage in actual architectural debates. The key? Keep them focused on producing a comprehensive project specification—not jumping straight to code.
The Documentation Discipline
Here’s where most developers stumble: They treat AI like a magic code generator rather than a team member who needs clear direction. The solution? Obsessive documentation.
Before writing a single line of code, I now demand that my AI architects generate a complete project specification. Pro tip: Activate “Deep Research” mode (when available) for this step—the difference in thoroughness is striking. But always verify the output; even the best models can confidently document features that do not exist.
The Chunking Strategy That Changes Everything
System design experience becomes crucial here. Break your project into major functional phases, or as I like to say chunks! Then document this breakdown in `./docs/implementation-plan.md`. Why? Because AI models, like junior developers, excel when given focused, well-defined tasks rather than vague mandates to “build the thing.”
Within each coding session, I further decompose major chunks into 4-5 sub-chunks. This granular approach, combined with frequent Git commits, creates natural rollback points when the AI inevitably suggests something creative but catastrophic.
Documentation as a Control Mechanism
The real breakthrough came when I started treating documentation not as an afterthought but as the primary control mechanism for AI behavior. Tools like Claude Code or Windsurf can analyze existing codebases and generate comprehensive documentation—from updated README files to architectural notes in `./docs`. I even have them scan Git history to auto-generate CHANGELOG files.
The magic happens when you make documentation updates part of your workflow. After completing each major chunk, pause and command your AI to update all documentation with implementation changes. This creates a feedback loop that keeps the AI aligned with your actual architecture rather than its imagined ideal.
AI Agents Across the SDLC
This approach of orchestrating LLM assistants as a Tech Lead mirrors a broader industry movement. An emerging ecosystem of Cognitive DevOps vendors is assembling specialized AI agents for each phase of the SDLC, effectively allowing organizations to build their own “SDLC agent swarm.” In the upfront stages, generative tools are tackling requirements and design.
AuctorAI – a recent Y Combinator startup – focuses on automating pre-sales and requirements development, turning initial business needs into formal project specs via cognitive DevOps agents. Cloobot, backed by Alchemist Accelerator, offers an AI co-pilot for solution architects: a multi-agent system that shepherds a project from discovery to deployment by monitoring design and early deployments, diagnosing issues like merge conflicts, and auto-remediating them – an approach reported to cut implementation timelines by roughly 4×.
By rapidly translating stakeholder intent into actionable user stories and plans, these tools demonstrate Tech Lead–style delegation, with AI agents handling the grind of requirement gathering and solution design.
During implementation and testing, other vendors have carved out niches for AI-driven execution. Ressl AI acts as a “virtual Salesforce admin,” scanning an org’s metadata for needed changes and using multiple agents to generate refactoring plans and implement configurations up to 10× faster than manual work. Likewise, Cirra AI’s Change Agent interprets plain-English configuration requests and autonomously executes the required Salesforce changes (with human approvals when needed for safety). Quality assurance is no longer purely human labor either: TestZeus is crafting a GenAI QA co-pilot that can auto-write, run, and self-heal test cases across Salesforce environments, while Testsigma recently integrated generative AI to automatically create and maintain entire end-to-end test suites (self-healing tests as configs evolve and suggesting new test steps), minimizing the need for manual QA input.
In effect, these development and testing agents work as tireless junior developers and QA engineers, ensuring that code implementation and validation keep pace without bottlenecks.
Even the traditionally human-intensive realms of deployment, operations, and governance are being augmented by AI teammates. SRE.ai exemplifies an AI-first release engineer: its platform lets teams deploy changes and configure workflows through natural-language commands and best-practice templates, anticipating and resolving deployment issues before they become bottlenecks. Established DevOps players are also infusing AI into their toolchains. For example, Copado, a leading Salesforce DevOps provider, has embedded a generative AI assistant that helps dev teams with everything from auto-generating test cases and refining user stories to running compliance checks and suggesting optimal CI/CD deployment steps.
Similarly, Opsera’s DevOps orchestration platform now includes a Hummingbird AI module to unify pipelines across any tech stack and to provide predictive insights (like AI-enhanced DORA metrics) at each stage of delivery, enabling faster, safer releases with end-to-end visibility. Overseeing this growing agent swarm are tools geared towards governance and intelligence. Hubbl Technologies functions as a DevOps “workbench” that pairs process mining insights with AI copilots – for instance, analyzing a Salesforce org’s health to suggest optimizations and even trigger automated changes via partners. And Elements.cloud serves as a change intelligence layer, leveraging AI for impact analysis and design validation: it generates requirement-driven impact assessments and recommends refactoring steps to manage technical debt before deployment.
Together, this cadre of AI-powered assistants is transforming software delivery into a coordinated multi-agent operation. Forward-thinking engineering leaders can mix and match these specialized “virtual team members” to assemble their own SDLC agent swarm – a configuration of AI experts that parallels a traditional team’s roles, from analyst and architect to tester, DevOps engineer, and SRE – all managed with the same clarity of vision and documentation that a great Tech Lead would apply to human teams.
The Bottom Line
Thinking like a Tech Lead managing my team has fundamentally shifted how I view AI coding assistants. They’re not magical productivity multipliers out of the box; they’re powerful but unruly team members who need active management. By adopting a Tech Lead mindset, creating comprehensive specifications, chunking work intelligently, and maintaining rigorous documentation, you transform these tools from unpredictable code generators into reliable development partners.
The investment in upfront planning and continuous documentation pays dividends. Not only does it keep your AI assistants on track, but it also creates a living project history that makes onboarding human developers exponentially easier.
And in the not-too-distant future, we can look forward to these agentic coding and software development lifecycle agents becoming packaged and available as commercial software within the Salesforce ecosystem.
Executive Takeaway: If your development team is experimenting with AI coding tools, mandate documentation-first workflows and phase-based development cycles. The 20% extra time spent on specification and documentation will save 50% in debugging and refactoring costs down the line.
Ready to implement this approach? Start with a single project: Generate a comprehensive specification using a high-tier AI model, create an implementation plan with 3-5 major chunks, and commit to updating documentation after each chunk completion. Measure the difference in code quality and development velocity after 30 days.
