agentic-development

Set up an agentic development workflow by defining clear task orchestration, selecting models matched to your project scale, structuring prompts for consistency, and managing context through visual inputs like screenshots and CLI tools. Start with a single agent to establish baseline performance, then parallelize workflows once patterns stabilize.

Choose models by evaluating cost-per-token, latency requirements, and output quality for your specific tasks. For high-volume projects, balance model capability against subscription economics—smaller models often suffice for structured tasks, while complex reasoning benefits from larger models. Profile real workloads before committing.

Improve output quality by optimizing prompt strategies for each agent role, using visual context like screenshots to reduce ambiguity, managing token budgets to preserve relevant context, and coordinating parallel agents through clear handoff protocols. Test incrementally and measure output consistency across high-volume runs.

Use CLI tools when agents need direct access to file systems, version control, or environment state with immediate feedback. Use MCPs when standardized protocol integration is available and reduces context overhead. CLI tools typically beat MCPs for context fidelity in development automation.

Yes, automate development tasks by starting with well-defined, single-agent workflows, measuring performance against actual bottlenecks, then scaling. Focus on prompt clarity and context management before tuning orchestration or parallelization—this prevents over-engineering and accelerates time to working code.

Parallel agents face constraints: context duplication increases token costs, coordination overhead grows with agent count, and debugging becomes complex. Start with sequential workflows or loosely-coupled parallel tasks; only parallelize when bottlenecks are measured and handoff protocols are validated.