Joint training for POs, designers, and developers: align the team so AI-driven code generation compounds — higher throughput across the codebase.
Buying Cursor, Antigravity, or Copilot does not install org-wide code generation velocity. Without shared pipelines — what agents generate, from what source of truth, under what release bar — you get parallel chats and local spikes that flatline when everyone ships together. We add structure so agent output scales with your team and merge cadence, not just faster individual cycles.
agentic coding and agentic engineering only scale when product, design, and engineering agree where truth lives, what “done” means for an agent run, and how you verify before merge. We run joint sessions so prompts, tools, and hand-offs match — no hero prompts nobody else can run.
Models are everywhere; a practiced, org-wide habit for agentic coding before merge usually is not.
Joint training aligns POs, designers, and developers on how agentic coding compounds at real merge volume.
Working patterns in your environment — not slide decks. People who have shipped production systems sit on your real tasks so agentic coding matches how you release today.
€1,249
Half-day – leadership cohort (unlimited participants)
For managers and leads who need a shared picture before funding a multi-day block. We connect market pressure, quality risk, and what professional agentic coding and agentic engineering require — then you leave with a clear go, park, or scale decision.
Includes
Recommended
€4,995
3–4 days – up to 15 participants
The hands-on format most teams start with: your engineers build working agent loops, layered context, and review rituals directly on your repository — led as serious engineering work, not generic tool tips.
Includes
Maximum depth
€16,800
3–4 days
When your risks and workflows need more than a standard run: we shape the block to your company, then combine shared whole-team time with private sessions per role so each function gets a dedicated deep dive — not only a single mixed track. The development team workshop stays deliberately coding- and implementation-led; the immersion adds that common-plus-individual structure so depth is higher across roles. Still 3–4 days with everything versioned in your repo.
Includes
Development team workshop curriculum
Led by practitioners with 20+ years of architecture and engineering experience — not AI tool trainers. Hands-on with your actual codebase and product challenges. Your team builds working agents, AI-powered workflows, and practices they deploy immediately.
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.
From discovery to light follow-up — four clear phases so your team always knows what happens next.
We map stack, compliance, and where agentic coding already leaks time. You pick success signals with your leads so the workshop hits real bottlenecks — not toy examples.
3–4 days on your codebase: design prompts, wire MCP where it earns its keep, and run review loops together. You ship small, real improvements during the block so habits survive Monday morning.
Artifacts land as Markdown, rules, and scripts next to the code — versioned like any other change. Your team keeps owning the system; we do not gate knowledge behind a separate subscription product.
A concise wrap-up call and written recommendations — optional deeper mentoring only if you ask for it. No bundled “content subscription”; your materials live with you.
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.
Deep Deeper
Plain-language primer: what SDD names, how the loop runs, why agentic coding brought it back, how PRDs and SRS-style docs fit as inputs—then contrasts, workflow depth, and Karpathy context.
Scope, logistics, and how we plug into your week.
Still unsure? Email us and we will answer directly.
Agentic team training
Book a strategy call to align scope, tier, and dates — so you can defend the plan internally without another week of email.
~25 minutes on video. Your stack, pilot vs full cohort, and what “done” looks like.
Book a strategy callMulti-day blocks are capped each quarter — Q3 2026 dates move first.