[{"data":1,"prerenderedAt":281},["ShallowReactive",2],{"blog-post-blog_en-agentic-sdlc-fuer-softwareteams":3},{"id":4,"title":5,"body":6,"cover":265,"date":266,"description":267,"draft":268,"extension":269,"meta":270,"navigation":271,"path":272,"seo":273,"stem":274,"tags":275,"__hash__":280},"blog_en\u002Fen\u002Fblog\u002Fagentic-sdlc-fuer-softwareteams.md","Agentic SDLC for Software Teams: Bringing AI Work Into Delivery Safely",{"type":7,"value":8,"toc":260},"minimark",[9,18,23,26,29,32,66,69,73,76,83,202,205,237,240,244,247,256],[10,11,12,13,17],"p",{},"Agentic SDLC is becoming relevant for software teams because AI agents no longer only suggest code. They now work across requirements, implementation, tests, review and documentation. The bottleneck therefore moves from code generation to the question of ",[14,15,16],"strong",{},"which work an agent may contribute to the delivery process, and with which evidence",".",[19,20,22],"h2",{"id":21},"what-agentic-sdlc-means-in-practice","What Agentic SDLC Means in Practice",[10,24,25],{},"Agentic SDLC is not a new process diagram. It is a delivery mode where AI agents are connected to existing engineering steps under control: understanding a ticket, planning a change, editing code, running tests, preparing a pull request and providing review context.",[10,27,28],{},"The difference from individual coding assistants is accountability. An Agentic SDLC needs clear rules for assignment, execution, evidence and approval.",[10,30,31],{},"For growing teams, these building blocks matter most:",[33,34,35,42,48,54,60],"ul",{},[36,37,38,41],"li",{},[14,39,40],{},"Work assignment:"," Tickets, specifications and acceptance criteria must be concrete enough that the agent does not have to guess product decisions.",[36,43,44,47],{},[14,45,46],{},"Execution environment:"," Repository access, network access, secrets and file scopes need technical boundaries, not only policy documents.",[36,49,50,53],{},[14,51,52],{},"Evidence:"," Tests, logs, diff summaries and known risks must be visible in the pull request.",[36,55,56,59],{},[14,57,58],{},"Approval:"," Humans remain responsible for domain logic, architecture boundaries, security and production-relevant decisions.",[36,61,62,65],{},[14,63,64],{},"Learning loop:"," Failed agent tasks need to feed back into better specs, tests and guardrails.",[10,67,68],{},"The value is therefore not autonomy by itself. Value appears when agents take on recurring, verifiable tasks and the team gains more time for product and architecture decisions.",[19,70,72],{"id":71},"where-teams-should-start-with-release-gates","Where Teams Should Start With Release Gates",[10,74,75],{},"The most common mistake is an overly broad pilot. If agents work on new features, refactorings, tests and documentation across several repositories at once, nobody can measure cleanly whether quality, review effort or risk improves.",[10,77,78,79,82],{},"A useful starting point is a small set of ",[14,80,81],{},"release gates"," for agent work:",[84,85,90],"pre",{"className":86,"code":87,"language":88,"meta":89,"style":89},"language-yaml shiki shiki-themes github-light github-dark","agentic_sdlc_gate:\n  allowed_tasks: [\"test_coverage\", \"small_bugfix\", \"documentation\"]\n  required_evidence: [\"ci_passed\", \"diff_summary\", \"risk_note\"]\n  human_approval: [\"production_logic\", \"auth\", \"data_migration\"]\n  blocked: [\"secrets\", \"billing_rules\", \"legal_terms\"]\n","yaml","",[91,92,93,106,133,156,179],"code",{"__ignoreMap":89},[94,95,98,102],"span",{"class":96,"line":97},"line",1,[94,99,101],{"class":100},"s9eBZ","agentic_sdlc_gate",[94,103,105],{"class":104},"sVt8B",":\n",[94,107,109,112,115,119,122,125,127,130],{"class":96,"line":108},2,[94,110,111],{"class":100},"  allowed_tasks",[94,113,114],{"class":104},": [",[94,116,118],{"class":117},"sZZnC","\"test_coverage\"",[94,120,121],{"class":104},", ",[94,123,124],{"class":117},"\"small_bugfix\"",[94,126,121],{"class":104},[94,128,129],{"class":117},"\"documentation\"",[94,131,132],{"class":104},"]\n",[94,134,136,139,141,144,146,149,151,154],{"class":96,"line":135},3,[94,137,138],{"class":100},"  required_evidence",[94,140,114],{"class":104},[94,142,143],{"class":117},"\"ci_passed\"",[94,145,121],{"class":104},[94,147,148],{"class":117},"\"diff_summary\"",[94,150,121],{"class":104},[94,152,153],{"class":117},"\"risk_note\"",[94,155,132],{"class":104},[94,157,159,162,164,167,169,172,174,177],{"class":96,"line":158},4,[94,160,161],{"class":100},"  human_approval",[94,163,114],{"class":104},[94,165,166],{"class":117},"\"production_logic\"",[94,168,121],{"class":104},[94,170,171],{"class":117},"\"auth\"",[94,173,121],{"class":104},[94,175,176],{"class":117},"\"data_migration\"",[94,178,132],{"class":104},[94,180,182,185,187,190,192,195,197,200],{"class":96,"line":181},5,[94,183,184],{"class":100},"  blocked",[94,186,114],{"class":104},[94,188,189],{"class":117},"\"secrets\"",[94,191,121],{"class":104},[94,193,194],{"class":117},"\"billing_rules\"",[94,196,121],{"class":104},[94,198,199],{"class":117},"\"legal_terms\"",[94,201,132],{"class":104},[10,203,204],{},"That turns into concrete leadership and architecture decisions:",[33,206,207,213,219,225,231],{},[36,208,209,212],{},[14,210,211],{},"Define task classes:"," Which tasks can be delegated, and which stay with experienced developers for now?",[36,214,215,218],{},[14,216,217],{},"Extend the definition of done:"," An agent pull request is only ready for review when tests, risk notes and business assumptions are documented.",[36,220,221,224],{},[14,222,223],{},"Limit permissions:"," Agents need access to the necessary context, but not broad access to critical systems.",[36,226,227,230],{},[14,228,229],{},"Maintain evals and tests:"," Recurring agent tasks need automated checks, otherwise manual review effort increases.",[36,232,233,236],{},[14,234,235],{},"Define ownership:"," Product, engineering and security need to know who assesses an agent failure and adjusts the rule.",[10,238,239],{},"An Agentic SDLC should first grow around tasks that are easy to verify. Only when review time, defect rate and rework stay stable does it make sense to expand into more complex product logic.",[19,241,243],{"id":242},"why-this-matters","Why This Matters",[10,245,246],{},"Agentic SDLC matters economically because AI agents can reduce implementation cost, but they do not automatically reduce the cost of understanding, quality assurance and operations. Without release gates, work merely shifts from writing code into review, debugging and incident response.",[10,248,249,250,255],{},"For founders, product leaders and engineering managers, this is a leadership issue: agents need to be embedded into the delivery process, not run beside it. Teams that define evidence, permissions and approvals early can delegate more work without weakening architecture, compliance or team trust. 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What growing software teams should clarify before production use.",false,"md",{},true,"\u002Fen\u002Fblog\u002Fagentic-sdlc-fuer-softwareteams",{"title":5,"description":267},"en\u002Fblog\u002Fagentic-sdlc-fuer-softwareteams",[276,277,278,279],"AI","Software Quality","Engineering Leadership","Software Architecture","xq9w-qw6Cop4Xv8JyVmfjPusew3qHCGpOQ1diuaW6hI",1783430349766]