Prism is a multi-agent AI DevSecOps platform implementing the AI-Driven Development Life Cycle (AI-DLC). We help engineering teams on AWS, Azure, and Google Cloud ship faster and safer — covering all nine SDLC phases, from sprint planning to production incidents, with a continuous feedback loop that makes the platform smarter every sprint.
Today's DevSecOps stack is a patchwork of point solutions — one tool for code, another for pipelines, another for incidents, another for security. AI is being layered on top of each silo, but the AI in one silo never learns from the AI in another. Engineering teams end up stitching together a dozen disconnected tools and still doing most of the work themselves.
Modern engineering teams stitch together nine to twelve disconnected tools across the SDLC. AI assistance exists in isolated pockets — a code suggestion here, an incident summary there. No tool understands the full journey from sprint story to production incident, so insight in one stage never compounds into intelligence in the next.
Most AI in the DevSecOps space is advisory only. It tells engineers what to do; engineers still have to do it. When a production incident fires at 2am, what the team needs is not a suggestion in a chat window — it's a verified, human-approved action taken immediately, with a full audit trail.
When an incident resolves, the knowledge stays in a Slack thread and a few minds. Six months later, the next similar incident starts from zero. No platform today builds a knowledge graph that grows with your team's actual history and feeds back into the AI's decisions, so the same mistakes get re-discovered every quarter.
AI DevOps tooling tends to be tightly coupled to a single hyperscaler and a single foundation model. Teams have no realistic path to choose their LLM, keep data in their own tenant, or move between clouds without rebuilding from scratch — even when the business case demands it.
Prism implements the AI-Driven Development Life Cycle — a methodology where AI is not a co-pilot for individual tasks but an active, executing participant in every phase of software delivery. The platform operates through a repeating Plan → Execute → Verify loop across all nine SDLC phases. Every decision Prism makes is scored nightly against its actual outcome.
Manual PRDs, gut-feel estimates, planning meetings that produce docs nobody reads.
AI translates business intent into living specifications. Story estimates are backed by 6 sprints of real ADO velocity data. ADRs are generated from design briefs and cross-checked against your team's existing conventions. Infrastructure cost is estimated before a line of code is written.
Developers writing code, tests, and docs from scratch. PR reviews inconsistent. Security checked at the end, not continuously.
Every PR reviewed in 60 seconds — security issues, logic errors, naming convention violations flagged as inline comments. Terraform generated from natural language and refined through a conversational PR comment loop. Test suites generated overnight. Security gates run at every SDLC boundary, not just pre-deploy.
Reactive monitoring. Alert storms. RCA takes hours. Each incident starts from scratch.
RCA starts the moment an alert fires. Root cause identified in seconds using the incident knowledge graph. Canary analysis compares baseline vs new version automatically — auto-rollback triggered on anomaly. The feedback loop scores every AI decision nightly. Prompts improve weekly. The platform compounds in value every sprint.
A simplified view of the core loop. SDLC signals arrive at the Planner, the Verifier scores blast-radius, the Executor commits verified change, and the outcome feeds back into the next planning cycle. Press + Signal to inject one yourself.
Six things every engineering team using Prism gets out of the box, regardless of which cloud they run on.
Every pull request reviewed by Prism in under a minute. Inline comments on security issues, logic errors, missing tests, and convention violations — before a human reviewer opens it.
Story estimates grounded in six sprints of your team's real delivery data. No more gut-feel guesses or planning meetings that produce shelf-ware documentation.
Every deploy gets a canary slice. Prism watches the metrics, compares baseline to new version, and rolls back automatically on anomaly — before customers notice.
ADRs and architecture decisions generated from design briefs and cross-checked against your team's existing conventions. Docs that stay in sync with what's actually deployed.
Security checks at every SDLC boundary — not just pre-deploy. Vulnerabilities flagged at PR, infrastructure changes scanned before apply, runtime alerts triaged in seconds.
Every decision Prism makes is scored nightly against its actual outcome. Prompts and policies refine weekly. The platform measurably gets better at understanding your team every sprint.
The Prism architecture is identical on AWS, Azure, and GCP. Only the managed services change. Every agent communicates async via the message bus — never direct HTTP. The Verifier is the safety net that sits between reasoning and execution.
Every SDLC event enters Prism through a normalised message envelope. Source-control webhooks, CI/CD pipeline results, cloud monitoring alerts, and security findings are published to 9 dedicated topics — one per SDLC phase. HMAC-SHA256 signature validation on every inbound event. At-least-once delivery. Seven-day retention with a dead-letter queue on every subscription.
Three agents operating async via the message bus. The Planner Agent (LangGraph) reads signals and generates structured action plans with confidence scores. The Verifier Agent scores each plan against a YAML blast-radius configuration — always warm, always fail-closed. The Executor Agent is a deterministic tool caller with zero LLM reasoning — it only runs verified, approved plans. Human approval gates route high-risk actions to Teams or Slack before execution.
A Model Context Protocol (MCP) server exposing 24 tools across five domains — source-control APIs, cloud infrastructure, Terraform execution, notifications, and runbook utilities. Two human-in-the-loop modes: approve/reject buttons in Teams or Slack for high blast-radius actions; conversational PR comment loop for Terraform and refineable changes. Approval keywords are matched as exact strings from a YAML config — never LLM inference — preventing prompt injection via malicious PR descriptions.
Identity — one managed identity per agent, no shared identities, no wildcard permissions, OIDC federation for CI/CD with zero stored secrets. Knowledge — sprint velocity in the cloud-native data warehouse, runbooks and ADRs in a managed RAG knowledge layer with content-hash invalidation, real-time agent state in a low-latency document store. Feedback Loop — a nightly scoring job compares every AI decision against its actual outcome. Quality scores are retained indefinitely as institutional memory; prompts are refined weekly by a human engineer.
Prism sits at the intersection of three large, fast-growing markets — DevSecOps platforms, AI for software development, and platform engineering. Each is already in the tens of billions; together they are being re-platformed around agentic AI right now.
Prism's defensibility comes from three structural choices that compound over time.
Tool-using behaviour, multi-step reasoning, and structured output are now production-grade. A modern frontier model can execute a thirty-step DevOps plan reliably enough to trust with real infrastructure. Two years ago this was a research demo.
Model Context Protocol is now backed by every major LLM vendor. Tools that took months to expose with custom plumbing can be wired in days with consistent semantics across providers — making a portable agent platform realistic for the first time.
Amazon Bedrock, Azure OpenAI, and Google Vertex AI now offer comparable agentic primitives. Enterprises can pick the cloud they already run on and get the same caliber of model and tooling — if the platform on top is built to span all three.
Anand is the architect of the AI-DLC platform. He designed Prism's three-agent core — Planner, Verifier, Executor — around a single principle: AI should execute verified change, not just suggest it. He defined the blast-radius scoring model, the fail-closed verifier pattern, and the two human-in-the-loop modes that make agentic action safe in production. The reference implementation runs on AWS, Azure, and Google Cloud from the same agent code, with zero static credentials anywhere in the stack.
Defined the nine-phase coverage model, the Plan → Execute → Verify loop, and the nightly scoring job that turns every decision into training signal for the next sprint.
Planner (LangGraph), always-warm fail-closed Verifier, and zero-LLM deterministic Executor. The MCP server exposing 24 tools across source-control, infrastructure, Terraform, notifications, and runbooks.
Identical agent architecture on AWS (Bedrock + AgentCore + Lambda), Azure (OpenAI + AI Foundry + Container Apps), and Google Cloud (Vertex AI + Agent Engine + Cloud Run). Zero static credentials — OIDC federation end-to-end.
RelayOps.ai is an independent company building the Prism AI-DLC platform. If you run an engineering team on AWS, Azure, or Google Cloud and want to talk about a pilot, an evaluation, or just see the platform in action — email is the fastest path. Replied to personally within 24 hours.