{"slug": "beyond-ci-cd-building-agentic-validation-and-inspection-layers-with-langgraph", "title": "Beyond CI/CD: Building Agentic Validation and Inspection Layers with LangGraph, MCP, and A2A", "summary": "A developer proposes an agentic validation and inspection layer built with LangGraph, MCP, and A2A to complement traditional CI/CD pipelines. The architecture combines deterministic tools like SQL linters and SAST scanners with stateful reasoning agents that evaluate deployment context and risk before production releases.", "body_md": "Modern CI/CD pipelines are exceptionally good at automation.\n\nThey compile code, run tests, build artifacts, scan dependencies, deploy infrastructure, and promote releases.\n\nBut there is an important difference between automating a deployment and reasoning about whether a change should be deployed.\n\nConsider a pull request containing:\n\nDROP TABLE customer_transactions;\n\nOr:\n\nDELETE FROM orders;\n\nOr an application change that accidentally:\n\nremoves authorization middleware,\n\nintroduces an unsafe database operation,\n\nexposes a secret,\n\nchanges an infrastructure security boundary,\n\nbypasses an architectural convention,\n\ndeploys a breaking schema migration,\n\nor modifies production resources outside the expected scope.\n\nTraditional CI/CD can detect many of these problems when explicit rules already exist.\n\nThe harder problem is determining contextual risk.\n\nIs this DROP statement part of an approved migration?\n\nDoes a DELETE contain an appropriate predicate?\n\nDoes the changed service still satisfy the system's architectural constraints?\n\nDoes the migration preserve compatibility with applications currently running in production?\n\nDoes a seemingly harmless configuration change expand the attack surface?\n\nThis is where an agentic inspection layer can complement—not replace—traditional CI/CD security controls.\n\nThe idea is simple:\n\nBefore production deployment, introduce a stateful reasoning layer that collects evidence from deterministic tools, evaluates the change from multiple perspectives, and produces an auditable deployment decision.\n\nA practical architecture can combine:\n\ndeterministic linters and security scanners,\n\nLangGraph and StateGraph,\n\nspecialized validation agents,\n\nModel Context Protocol (MCP),\n\nAgent2Agent (A2A) communication,\n\npolicy engines,\n\nCI/CD status checks,\n\nand human approval for high-risk operations.\n\nThis article develops that architecture from first principles.\n\nA conventional pipeline often resembles:\n\nDeveloper\n\n↓\n\nPull Request\n\n↓\n\nLint\n\n↓\n\nUnit Tests\n\n↓\n\nSAST / Dependency Scan\n\n↓\n\nBuild\n\n↓\n\nDeploy Test\n\n↓\n\nDeploy Production\n\nThis architecture is essential.\n\nNIST's Secure Software Development Framework recommends integrating secure development practices throughout the software development lifecycle rather than treating security as a final-stage activity.\n\nOWASP similarly emphasizes that CI/CD infrastructure is itself security-sensitive because pipelines frequently have access to source code, credentials, artifacts, infrastructure, and deployment environments.\n\nThe problem is therefore not that traditional CI/CD is obsolete.\n\nThe problem is that most controls are intentionally specialized.\n\nA SQL linter understands SQL.\n\nA SAST engine understands known code patterns and data flows.\n\nA dependency scanner understands packages and vulnerabilities.\n\nA unit test validates expected behavior.\n\nNone necessarily understands the complete deployment context.\n\nThat distinction motivates a second layer.\n\nAn important architectural rule is:\n\nNever ask an LLM to replace a deterministic control when a deterministic control can reliably perform the job.\n\nFor example, SQL syntax should normally be validated using a SQL parser or linter.\n\nSQLFluff provides dialect-aware SQL parsing and linting and can also work with templated SQL.\n\nSimilarly, source-code security analysis should continue using tools such as CodeQL, Semgrep, dependency scanners, secret scanners, and language-native test frameworks.\n\nThe agentic layer sits above those tools.\n\nInstead of asking:\n\nLLM → Is this SQL valid?\n\nprefer:\n\nSQL Parser\n\n↓\n\nSQL Linter\n\n↓\n\nPolicy Rules\n\n↓\n\nAgentic Risk Analysis\n\nThe first three stages generate evidence.\n\nThe agent interprets that evidence within the broader deployment context.\n\nThis gives us a hybrid architecture:\n\nDeterministic Controls\n\n+\n\nContextual Agentic Reasoning\n\n+\n\nHuman Governance\n\nThat combination is considerably safer than treating an LLM as a universal security scanner.\n\nA useful high-level architecture is:\n\n```\n                ┌───────────────────┐\n                │   Pull Request    │\n                └─────────┬─────────┘\n                          │\n                          ▼\n                ┌───────────────────┐\n                │ Change Detection  │\n                └─────────┬─────────┘\n                          │\n                          ▼\n                ┌───────────────────┐\n                │ Gatekeeper Agent  │\n                └─────────┬─────────┘\n                          │\n         ┌────────────────┼────────────────┐\n         ▼                ▼                ▼\n  Validation Agent   Security Agent    Review Agent\n         │                │                │\n         └────────────────┼────────────────┘\n                          ▼\n                ┌───────────────────┐\n                │ Policy Evaluation │\n                └─────────┬─────────┘\n                          │\n                ┌─────────┴─────────┐\n                ▼                   ▼\n             APPROVE              BLOCK\n                │                   │\n                ▼                   ▼\n           Deployment        Human Review\n```\n\nI generally divide the system into four logical responsibilities.\n\nGatekeeper\n\nThe Gatekeeper determines what changed and what inspection path is required.\n\nExamples:\n\nPython changed\n\n→ Python validation + security inspection\n\nSQL changed\n\n→ SQL parser + SQL policy inspection\n\nTerraform changed\n\n→ IaC security inspection\n\nDockerfile changed\n\n→ container security inspection\n\nThe Gatekeeper should not blindly analyze the entire repository for every commit.\n\nIts first responsibility is scope reduction.\n\nThe Validation Agent answers:\n\nIs the proposed change structurally valid?\n\nFor Python, this might include:\n\nruff check .\n\npytest\n\nmypy .\n\nFor SQL:\n\nsqlfluff lint migrations/\n\nFor infrastructure:\n\nterraform validate\n\nFor containers:\n\ndocker build .\n\nThe important point is that the agent does not invent validation.\n\nIt orchestrates established tools and normalizes their results.\n\nConceptually:\n\nvalidation_result = {\n\n\"syntax\": \"PASS\",\n\n\"tests\": \"PASS\",\n\n\"lint\": \"PASS\",\n\n\"violations\": []\n\n}\n\nThat becomes evidence for subsequent agents.\n\nThe Security Agent focuses on exploitable or operationally dangerous changes.\n\nIts inputs may include results from:\n\nCodeQL\n\nSemgrep\n\nSecret Scanner\n\nDependency Scanner\n\nIaC Scanner\n\nSQL Policy Engine\n\nContainer Scanner\n\nFor example:\n\nsecurity_result = {\n\n\"critical\": 0,\n\n\"high\": 1,\n\n\"medium\": 3,\n\n\"secrets_detected\": False\n\n}\n\nThe agent can then evaluate those findings alongside the actual code diff.\n\nThis distinction matters.\n\nA scanner detects findings.\n\nThe agent helps reason about their deployment significance.\n\nDatabase changes are particularly interesting because perfectly valid SQL can still be operationally catastrophic.\n\nConsider:\n\nDELETE FROM customer_orders;\n\nThis may be syntactically valid.\n\nBut the absence of a WHERE clause can make it extremely dangerous.\n\nLikewise:\n\nDROP DATABASE production;\n\nmay be syntactically valid while obviously requiring exceptional authorization.\n\nOther operations worth policy inspection include:\n\nDROP TABLE\n\nDROP SCHEMA\n\nTRUNCATE TABLE\n\nDELETE without WHERE\n\nUPDATE without WHERE\n\nALTER TABLE DROP COLUMN\n\nGRANT\n\nREVOKE\n\nCREATE OR REPLACE\n\nBut naive keyword blocking is insufficient.\n\nFor example:\n\nDROP TABLE temp_integration_test;\n\ninside an isolated ephemeral environment may be perfectly acceptable.\n\nTherefore the inspection decision should consider:\n\nStatement\n\n+\n\nEnvironment\n\n+\n\nObject classification\n\n+\n\nMigration context\n\n+\n\nPermissions\n\n+\n\nDependencies\n\n+\n\nPolicy\n\nThis is where agentic reasoning becomes useful.\n\nOne mistake I would avoid in production is implementing SQL security entirely with expressions such as:\n\nif \"DROP\" in sql.upper():\n\nreject()\n\nThis is fragile.\n\nSQL has:\n\ncomments,\n\naliases,\n\nnested statements,\n\nstored procedures,\n\ndialect differences,\n\ntemplating,\n\ndynamic SQL,\n\nquoted identifiers.\n\nA stronger architecture parses SQL into an Abstract Syntax Tree.\n\nConceptually:\n\nSQL\n\n↓\n\nLexer\n\n↓\n\nParser\n\n↓\n\nAST\n\n↓\n\nPolicy Engine\n\n↓\n\nRisk Evaluation\n\nFor example:\n\nDELETE\n\n├── target: customer_orders\n\n└── where: null\n\nNow the policy engine can reason structurally:\n\nif statement.type == \"DELETE\" and statement.where is None:\n\nrisk = \"CRITICAL\"\n\nThis is far more reliable than substring matching.\n\nA deployment gate is not naturally a single prompt.\n\nIt is a workflow.\n\nLangGraph models workflows using:\n\nstate,\n\nnodes,\n\nedges,\n\nconditional edges.\n\nThat maps remarkably well to CI/CD inspection.\n\nOur shared state could look conceptually like:\n\nclass PipelineState(TypedDict):\n\nchanged_files: list[str]\n\nvalidation_results: dict\n\nsecurity_results: dict\n\nsql_results: dict\n\narchitecture_results: dict\n\nrisk_score: int\n\ndecision: str\n\nThen define nodes:\n\ndetect_changes\n\nvalidate\n\ninspect_security\n\ninspect_sql\n\nreview_architecture\n\ncalculate_risk\n\ndeployment_gate\n\nAnd connect them:\n\nSTART\n\n↓\n\ndetect_changes\n\n↓\n\nvalidate\n\n↓\n\ninspect_security\n\n↓\n\ninspect_sql\n\n↓\n\nreview_architecture\n\n↓\n\ncalculate_risk\n\n↓\n\ndeployment_gate\n\n↓\n\nEND\n\nBut real pipelines need branching.\n\nThat is where StateGraph becomes especially useful.\n\nSuppose only documentation changed.\n\nRunning database inspection makes little sense.\n\nWe can route dynamically:\n\ndef route_change(state):\n\nfiles = state[\"changed_files\"]\n\n```\nif any(f.endswith(\".sql\") for f in files):\n    return \"sql_inspection\"\n\nif any(f.endswith(\".py\") for f in files):\n    return \"code_validation\"\n\nreturn \"lightweight_review\"\n```\n\nThe graph then represents deployment policy explicitly.\n\nConceptually:\n\n```\n            Change Detector\n                 │\n      ┌──────────┼───────────┐\n      ▼          ▼           ▼\n    SQL        Python       Docs\n      │          │           │\n      ▼          ▼           ▼\n SQL Agent   Code Agent   Lightweight\n      │          │           Review\n      └──────────┼───────────┘\n                 ▼\n              Review\n                 ↓\n              Decision\n```\n\nThis is substantially easier to audit than hiding the entire decision process inside one enormous system prompt.\n\nThe Review Agent asks a different question:\n\nEven if the change is technically valid, does it make architectural sense?\n\nImagine a service that previously used:\n\nAPI\n\n↓\n\nService Layer\n\n↓\n\nRepository\n\n↓\n\nDatabase\n\nA developer introduces:\n\nAPI\n\n↓\n\nDirect Database Query\n\nThe code may:\n\ncompile,\n\npass tests,\n\npass SQL linting,\n\ncontain no obvious vulnerability.\n\nBut it may violate an established architecture.\n\nA Review Agent can compare the diff against architecture policies stored in repository documentation or structured policy resources.\n\nFor example:\n\narchitecture/\n\nprinciples.md\n\nservice-boundaries.yaml\n\ndatabase-policy.yaml\n\nsecurity-policy.yaml\n\nThe output might be:\n\n{\n\n\"status\": \"WARN\",\n\n\"rule\": \"ARCH-DB-004\",\n\n\"reason\": \"API layer directly accesses persistence layer\",\n\n\"confidence\": 0.94\n\n}\n\nCritically, this should usually be treated as decision-support evidence, not unquestionable truth.\n\nOur agents need tools.\n\nThe SQL agent may need:\n\nSQL parser\n\nSchema metadata\n\nDependency graph\n\nMigration history\n\nThe Security Agent may need:\n\nSAST results\n\nDependency scanner\n\nSecret scanner\n\nRepository metadata\n\nThe Review Agent may need:\n\nArchitecture documents\n\nPolicy repository\n\nPull-request diff\n\nService catalog\n\nHard-coding every integration directly into every agent quickly becomes difficult to maintain.\n\nThis is where Model Context Protocol becomes useful.\n\nMCP defines a client-server architecture through which servers can expose primitives including:\n\ntools,\n\nresources,\n\nprompts.\n\nA conceptual deployment architecture becomes:\n\nAgent\n\n│\n\nMCP Client\n\n│\n\n├── Repository MCP Server\n\n├── SQL MCP Server\n\n├── Security MCP Server\n\n├── Policy MCP Server\n\n└── Deployment MCP Server\n\nFor example:\n\nSQL MCP Server\n\ntools:\n\nparse_sql\n\ninspect_schema\n\ncalculate_dependency_impact\n\nresources:\n\nschema://production\n\nmigrations://history\n\nThis creates a clean separation between:\n\nReasoning\n\nand:\n\nSystem Access\n\nThat separation is extremely valuable in production agent architectures.\n\nThis point deserves emphasis.\n\nMCP standardizes interaction.\n\nIt does not magically make every exposed operation trustworthy.\n\nThe MCP specification explicitly treats tool execution as security-sensitive and recommends strong user control, authorization, and data protection.\n\nTherefore a production CI/CD MCP server should enforce:\n\nAuthentication\n\nAuthorization\n\nLeast privilege\n\nInput validation\n\nAudit logging\n\nRate limiting\n\nEnvironment boundaries\n\nCredential isolation\n\nA SQL inspection agent, for example, should normally receive read-only metadata access.\n\nIt should not automatically receive credentials capable of executing:\n\nDROP DATABASE production;\n\nTool permissions must follow the principle of least privilege.\n\nMCP and A2A solve different architectural problems.\n\nA useful mental model is:\n\nMCP\n\nAgent ↔ Tools / Resources\n\nA2A\n\nAgent ↔ Agent\n\nA2A is designed for interoperability between independent agent systems.\n\nThat becomes useful when validation responsibilities are separated into independently deployed services.\n\nFor example:\n\n```\n                Orchestrator\n                     │\n          ┌──────────┼───────────┐\n          │          │           │\n          ▼          ▼           ▼\n      SQL Agent   Security    Architecture\n                    Agent        Agent\n```\n\nEach agent could be independently:\n\ndeployed,\n\nversioned,\n\nsecured,\n\nscaled,\n\nowned by another team,\n\nor implemented using a different agent framework.\n\nA2A provides a standardized interaction model between such agents.\n\nNow the architecture becomes more interesting.\n\n```\n                CI/CD Pipeline\n                      │\n                      ▼\n             LangGraph Orchestrator\n                      │\n             A2A Coordination Layer\n          ┌───────────┼───────────┐\n          ▼           ▼           ▼\n    Validation     Security      Review\n      Agent         Agent        Agent\n          │           │           │\n          └──── MCP Tool Layer ───┘\n                      │\n   ┌──────────────────┼─────────────────┐\n   ▼                  ▼                 ▼\n```\n\nRepository MCP Security MCP SQL MCP\n\n│ │ │\n\n▼ ▼ ▼\n\nGit Provider Code Scanners Database\n\nLangGraph manages workflow state.\n\nA2A handles communication between independently operating agents.\n\nMCP provides controlled access to tools and contextual resources.\n\nTraditional security tooling produces deterministic evidence.\n\nThe CI/CD platform enforces the final deployment gate.\n\nEach technology therefore has a distinct responsibility.\n\nThis may be the most important production rule in the article.\n\nDo not implement:\n\nif llm_response == \"SAFE\":\n\ndeploy_production()\n\nInstead:\n\nDeterministic Evidence\n\n↓\n\nAgentic Analysis\n\n↓\n\nPolicy Engine\n\n↓\n\nRisk Classification\n\n↓\n\nHuman Approval if Required\n\n↓\n\nCI/CD Gate\n\nThe final decision should be constrained by explicit policy.\n\nFor example:\n\nif critical_security_findings > 0:\n\ndecision = \"BLOCK\"\n\nelif destructive_sql_detected:\n\ndecision = \"HUMAN_APPROVAL\"\n\nelif test_coverage_failed:\n\ndecision = \"BLOCK\"\n\nelif architecture_risk == \"HIGH\":\n\ndecision = \"HUMAN_APPROVAL\"\n\nelse:\n\ndecision = \"PASS\"\n\nThe LLM contributes evidence and contextual reasoning.\n\nIt does not become an unrestricted production administrator.\n\nBinary pass/fail is sometimes insufficient.\n\nA better system can classify deployment risk.\n\nExample:\n\n0–20 LOW\n\n21–50 MEDIUM\n\n51–75 HIGH\n\n76–100 CRITICAL\n\nSignals might include:\n\nCritical vulnerability +50\n\nSecret detected +50\n\nDROP/TRUNCATE +40\n\nDELETE without WHERE +40\n\nBreaking schema change +30\n\nArchitecture violation +20\n\nInsufficient tests +15\n\nLarge dependency impact +15\n\nThen:\n\nLOW\n\n→ automatic continuation\n\nMEDIUM\n\n→ continue + warning\n\nHIGH\n\n→ mandatory reviewer approval\n\nCRITICAL\n\n→ deployment blocked\n\nThe exact weights should be calibrated using organizational incidents, false-positive analysis, and risk appetite rather than copied blindly from an example.\n\nAn agentic deployment gate should never simply return:\n\nDEPLOYMENT BLOCKED\n\nA useful result looks more like:\n\n{\n\n\"decision\": \"BLOCK\",\n\n\"risk\": \"CRITICAL\",\n\n\"score\": 92,\n\n\"findings\": [\n\n{\n\n\"file\": \"migrations/042_cleanup.sql\",\n\n\"line\": 18,\n\n\"type\": \"UNBOUNDED_DELETE\",\n\n\"evidence\": \"DELETE statement contains no WHERE clause\"\n\n}\n\n],\n\n\"required_action\": \"Add predicate or obtain destructive-operation approval\"\n\n}\n\nThis provides:\n\nevidence,\n\nlocation,\n\npolicy,\n\nseverity,\n\nremediation.\n\nThat transforms an AI gate from a mysterious reviewer into an auditable engineering control.\n\nSome operations should intentionally stop automation.\n\nFor example:\n\nDROP production table\n\nMajor IAM change\n\nDestructive migration\n\nHigh-confidence architecture violation\n\nProduction network change\n\nCritical scanner finding\n\nThe graph can interrupt:\n\nAgent Analysis\n\n↓\n\nHIGH RISK\n\n↓\n\nHuman Approval\n\n↙ ↘\n\nReject Approve\n\n↓ ↓\n\nSTOP Continue\n\nLangGraph's persistence and human-in-the-loop capabilities are particularly useful for workflows that must pause and later resume.\n\nThis makes the system appropriate for controlled production environments where automation must coexist with governance.\n\nPutting everything together:\n\nDeveloper Push\n\n↓\n\nPull Request\n\n↓\n\nChanged File Detection\n\n↓\n\nTraditional CI\n\n┌────┼─────┐\n\n│ │ │\n\nLint Test SAST\n\n└────┼─────┘\n\n↓\n\nAgentic Inspection Layer\n\n↓\n\nLangGraph StateGraph\n\n↓\n\nGatekeeper\n\n↓\n\n┌───────────────┐\n\n│ Parallel │\n\n│ Inspection │\n\n└───────────────┘\n\n↓ ↓ ↓\n\nValidation Security SQL\n\n↓ ↓ ↓\n\n└──────┼──────┘\n\n↓\n\nArchitecture Review\n\n↓\n\nPolicy Engine\n\n↓\n\nRisk Score\n\n↓\n\n┌────────┼────────┐\n\n▼ ▼ ▼\n\nPASS REVIEW BLOCK\n\n│ │\n\n│ Human\n\n│ Approval\n\n│ │\n\n└────────┘\n\n↓\n\nArtifact Build\n\n↓\n\nTest Deployment\n\n↓\n\nSmoke Tests\n\n↓\n\nProduction Gate\n\n↓\n\nProduction\n\nA good rule of thumb:\n\nProblem Preferred mechanism\n\nSyntax Parser/compiler\n\nFormatting Linter\n\nUnit behavior Tests\n\nKnown vulnerabilities SAST/SCA\n\nSecrets Secret scanner\n\nSQL structure SQL parser\n\nPolicy invariants Policy engine\n\nContextual impact Agent\n\nArchitecture reasoning Agent + policy\n\nCross-system investigation Agent\n\nFinal critical approval Human/policy\n\nThis separation is essential.\n\nAgentic systems become more trustworthy when they are surrounded by deterministic boundaries.\n\nIf I were implementing this architecture for an enterprise pipeline, I would consider these controls non-negotiable:\n\nAgents receive minimum required permissions.\n\nProduction inspection should prefer read-only access.\n\nLLM-generated SQL must never execute automatically merely because the model considers it safe.\n\nScanner findings remain authoritative evidence rather than being silently overridden by an LLM.\n\nCritical operations require deterministic policy gates.\n\nEvery agent decision should be logged with evidence and policy identifiers.\n\nPrompt injection must be considered because repository files, PR descriptions, comments, and documentation can all contain untrusted text.\n\nTool outputs must be validated before they enter downstream workflows.\n\nMCP tools should expose narrowly scoped capabilities rather than generic shell/database access.\n\nCredentials should be short-lived and environment-scoped wherever possible.\n\nAgent outputs should use structured schemas rather than unconstrained natural language.\n\nHigh-risk deployment decisions should support human review.\n\nThe interesting evolution is not:\n\nCI/CD → AI\n\nIt is:\n\nAutomation\n\n↓\n\nEvidence\n\n↓\n\nReasoning\n\n↓\n\nPolicy\n\n↓\n\nGovernance\n\n↓\n\nDeployment\n\nTraditional CI/CD answers:\n\nCan this software be built and deployed?\n\nAn agentic inspection layer adds another question:\n\nGiven the available evidence, organizational policy, architecture, environment, and potential blast radius, should this particular change proceed automatically?\n\nThat is a much richer engineering problem.\n\nAnd it is exactly why technologies such as LangGraph, MCP, and A2A become interesting in DevSecOps—not because we need an LLM inside every pipeline stage, but because complex delivery environments increasingly require stateful coordination across tools, policies, agents, and humans.\n\nConclusion\n\nAgentic CI/CD should not replace deterministic CI/CD.\n\nIt should sit above it as an inspection and decision-support layer.\n\nThe architecture I would advocate is:\n\nLangGraph\n\n→ stateful orchestration\n\nStateGraph\n\n→ explicit nodes, state, and conditional control flow\n\nMCP\n\n→ standardized access to tools and contextual resources\n\nA2A\n\n→ interoperability between independently deployed agents\n\nSAST / linters / parsers / tests\n\n→ deterministic technical evidence\n\nPolicy engine\n\n→ enforceable organizational rules\n\nHuman approval\n\n→ governance for high-risk operations\n\nThe result is not merely a pipeline that executes faster.\n\nIt is a pipeline capable of collecting evidence, understanding deployment context, escalating uncertainty, and protecting production boundaries before a risky change reaches them.\n\nThat is the direction I expect serious agentic DevSecOps architectures to move toward:\n\ndeterministic where possible, agentic where useful, and human-controlled where consequences matter.\n\nReferences\n\nNIST — Secure Software Development Framework (SSDF), SP 800-218\n\n[https://csrc.nist.gov/pubs/sp/800/218/final](https://csrc.nist.gov/pubs/sp/800/218/final)\n\nOWASP — CI/CD Security Cheat Sheet\n\n[https://cheatsheetseries.owasp.org/cheatsheets/CI_CD_Security_Cheat_Sheet.html](https://cheatsheetseries.owasp.org/cheatsheets/CI_CD_Security_Cheat_Sheet.html)\n\nLangChain — LangGraph Graph API / StateGraph\n\n[https://docs.langchain.com/oss/python/langgraph/graph-api](https://docs.langchain.com/oss/python/langgraph/graph-api)\n\nLangChain — LangGraph v1\n\n[https://docs.langchain.com/oss/python/releases/langgraph-v1](https://docs.langchain.com/oss/python/releases/langgraph-v1)\n\nModel Context Protocol — Architecture\n\n[https://modelcontextprotocol.io/docs/learn/architecture](https://modelcontextprotocol.io/docs/learn/architecture)\n\nModel Context Protocol — Specification\n\n[https://modelcontextprotocol.io/specification/2025-11-25](https://modelcontextprotocol.io/specification/2025-11-25)\n\nAgent2Agent Protocol — Specification\n\n[https://a2a-protocol.org/dev/specification/](https://a2a-protocol.org/dev/specification/)\n\nSQLFluff — SQL Linter Documentation\n\n[https://docs.sqlfluff.com/en/stable/](https://docs.sqlfluff.com/en/stable/)\n\nGitHub — CodeQL Code Scanning Documentation\n\n[https://docs.github.com/en/code-security/reference/code-scanning/codeql](https://docs.github.com/en/code-security/reference/code-scanning/codeql)\n\nOpenSSF — Scorecard\n\n[https://openssf.org/projects/scorecard/](https://openssf.org/projects/scorecard/)", "url": "https://wpnews.pro/news/beyond-ci-cd-building-agentic-validation-and-inspection-layers-with-langgraph", "canonical_source": "https://dev.to/nikhil_ramank_152ca48266/beyond-cicd-building-agentic-validation-and-inspection-layers-with-langgraph-mcp-and-a2a-30f0", "published_at": "2026-07-23 15:08:20+00:00", "updated_at": "2026-07-23 15:33:42.806682+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "developer-tools", "ai-safety", "ai-infrastructure"], "entities": ["LangGraph", "MCP", "A2A", "NIST", "OWASP", "SQLFluff", "CodeQL", "Semgrep"], "alternates": {"html": "https://wpnews.pro/news/beyond-ci-cd-building-agentic-validation-and-inspection-layers-with-langgraph", "markdown": "https://wpnews.pro/news/beyond-ci-cd-building-agentic-validation-and-inspection-layers-with-langgraph.md", "text": "https://wpnews.pro/news/beyond-ci-cd-building-agentic-validation-and-inspection-layers-with-langgraph.txt", "jsonld": "https://wpnews.pro/news/beyond-ci-cd-building-agentic-validation-and-inspection-layers-with-langgraph.jsonld"}}