EasySpecs Labs
The hands-on format most teams start with: your engineers build layered context, review rituals, and agentic coding workflows directly on your codebase — led as serious engineering work, not generic tool tips.
Merge throughput rises because the whole team rehearses the same repo-native context, rules, and verification rituals — agent output scales with your cadence instead of resetting every quarter when models or IDEs change.
Workshop curriculum
Four modules on the same spine as our public agentic coding curriculum — adapted to your stack, compliance, and review bar. Every exercise ships merge-worthy artifacts in your repo.
Browse full topic catalog (EB-1 … EB-11)Every EasySpecs Content Block and topic — combine ad hoc for your program (no fixed public duration).
We frame serious agentic coding with industrial metaphors you can teach and repeat. Together they describe a non-interactive way of working: you spend your time hardening how software is produced, not hand-authoring every variant of the product.
In AI-assisted development, the factory is what you version next to the code: rules, MCP tools, sub-agents, policies, and verification hooks that manufacture artifacts—patches, tests, migrations, docs—from structured intent. You implement and review the factory; the factory implements the feature-shaped output.
The production line is the ordered pipeline through your workstations: each step declares inputs and outputs (for example story → technical plan → patch → tests → human gate). Because the sequence is explicit, you can re-run the same line when priorities change and still land production-ready diffs.
A workstation is one narrow station on the line: a scoped agent run with its own prompt pack, tools, and acceptance checks—generate contract tests for this module, refactor under these invariants, summarize this delta for reviewers. Small blast radius, inspectable results.
Instead of “chat until it looks right,” teams invest in factories and lines that run with minimal babysitting—closer to a build than a conversation. When feature scope shifts, you do not chase the old hand-written path: you adjust factory inputs or a workstation, then repeat the process so the line emits a new production-ready solution. That is how delivery speed and flexibility compound as requirements move.
Your infrastructure makes any AI tool work reliably. Your multi-layer context architecture becomes your foundation. Your product teams structure domain knowledge as AI context. Your developers configure tools, MCPs, and sub-agents for consistent results. Your configurations are documented, customized, and ready to deploy immediately.
Your agents work through every development phase. Your POs produce complete specs from user stories. Your planning catches dependencies early. Your implementation follows standards consistently. Your reviews identify improvements systematically. Your legacy migrations gain momentum. Every phase becomes coordinated.
Your entire team transforms how they work together across development and product roles. Your practices refine sprint by sprint. Your team recognizes when to adapt agents and discovers new patterns. Your custom playbook and 90-day checklist guide your next steps.
Your patterns work brilliantly with AI. Your complex systems simplify into clear boundaries agents understand. Your APIs eliminate ambiguity. Your documentation feeds context naturally. Your architecture becomes the foundation that makes AI reliable, not a constraint.
When you want proof on your stack before a longer immersion, or when engineering leads need a fast, repo-native win leadership can fund without a four-week engagement.
Two weeks to pay back a €4,995 workshop
Conservative scenario: a team of eight engineers each saves three hours per week on review churn and context rebuilds after shared agent rituals land in the repo — roughly €4,995 equivalent in the first fortnight at typical loaded rates, then compounding each sprint.
Workshops are coached by senior EasySpecs practitioners — product, structured requirements, and platform architecture at the table with your team, not junior facilitators reading slides.
Lead coach — product & requirements · CEO, EasySpecs
Xesca combines product leadership with requirements engineering, helping teams turn business intent into explicit objectives and acceptance signals agents can work against—not vague chat prompts. She facilitates mixed rooms of engineering, product, and design while keeping a sharp definition of what “done” means.
Carlos Guirao Capistany
Lead coach — platform & agent workflows · CTO, EasySpecs
Carlos architects EasySpecs’ platform and AI systems with an emphasis on safe, reviewable change in mature codebases. He focuses on boundaries, APIs, and verification so agentic engineering produces diffs your team can trust—not opaque churn.
They join for context exercises where it helps. Engineering owns implementation depth; product and design focus on upstream intent so agents pull one source of truth.
Models will change; verifiable context, explicit objectives, and disciplined review will not. We teach durable habits behind agentic coding so your team can swap tools without restarting from zero.
You keep every artifact in your repository — prompts, rules, checklists, notes. We include a focused wrap-up conversation and written recommendations. Control Plane at Scale (€7,995) is a typical next step when orchestration and governance must scale org-wide; Context Engineering & Token Optimization (€4,995) follows in Optimize when token and context economics become the bottleneck.