AIToday
Large Language ModelsAI Coding AssistantsAI Business & IndustryAmazon AI BlogPublished: Aug 7, 2026, 04:01 JST3 min read

PDI Technologies builds no-code app deployer using AWS AI agents

PDI Technologies builds no-code app deployer using AWS AI agents

Key takeaway

  • PDI Technologies built PDI Brew, an AI-powered platform that lets non-technical employees request internal tools in plain English and receive fully provisioned web applications running on AWS within seconds.

  • The system uses two AI agents—one to capture user intent as a structured manifest, another on AWS Lambda to orchestrate resource creation—eliminating the need for developers, backlog slots, or deployment pipelines for small tools.

  • Every app inherits enterprise security, single sign-on, and optional governed AI capabilities through Amazon Bedrock, addressing the common problem of small but numerous internal tools that rarely get built.

3 Key Points

  1. What happened

    PDI Technologies created PDI Brew, a platform where non-technical employees describe tools in plain English and receive fully provisioned, multi-tenant web applications within seconds, secured with single sign-on and running on AWS. The system uses two AI agents: a planning agent that captures user intent as a structured JSON manifest, and a provisioning agent running on AWS Lambda that orchestrates the creation of all downstream AWS resources in one request.

  2. Why it matters

    Enterprise teams often have backlogs of small internal tools (calculators, intake forms, dashboards) that are too small to prioritize but too numerous to ignore. PDI Brew eliminates the traditional barrier by removing the need for Git, terminals, and DevOps knowledge—the person who needs the tool is the person who ships it. Every app inherits enterprise SSO, scoped security, HTTPS, and centralized observability by default, with optional governed AI capabilities (chat, summarize, classify) backed by Amazon Bedrock.

  3. What to watch

    The platform uses two interchangeable planning paths controlled by a single environment variable (PLANNER_MODE): the Vibe App Builder skill running inside an employee's existing AI assistant, or an Amazon Bedrock invocation inside the AWS trust boundary for teams with strict data-residency requirements. Both paths emit the identical JSON manifest, so the provisioning agent and per-app runtime remain unchanged regardless of which path is active.

Ask the AI about this article →

Context & Analysis

PDI Technologies identified a structural problem in enterprise software delivery: the transaction cost of building, securing, and deploying even a single-page tool (involving a repository, build pipeline, authentication, hosting, TLS, DNS, logging, and maintenance) is so high that small tools pile up in an indefinite backlog. PDI Brew solves this by collapsing that cost through automation and architectural consolidation. The platform separates intent capture from resource provisioning into two distinct agents with different trust models—the planning agent operates in the user's familiar AI assistant (or inside the AWS boundary for data-residency compliance), while the deterministic provisioning agent on Lambda handles all orchestration, logging, and reproducibility. This two-agent pattern is deliberate: it keeps the planning experience rich and conversational while ensuring that every provisioning decision is auditable and free of hallucination. By converging both planner paths on a single JSON manifest contract, PDI Brew ensures that adding new planning channels or swapping the underlying model requires no changes to the provisioning pipeline or per-app runtime—a clean forward path for future evolution.

FAQ

Do employees need technical skills to use PDI Brew?
No. A non-technical employee describes the tool they want in plain English, and within seconds they receive a fully provisioned, multi-tenant web application. No Git, terminals, or DevOps knowledge is required.
What types of apps can PDI Brew create?
The system classifies workloads into static apps (such as calculators or charts) and full-stack apps (that need data persistence). Static apps are wrapped with authentication and uploaded to S3; full-stack apps additionally provision a DynamoDB table, Lambda function, and API Gateway.
How is AI access controlled in deployed apps?
Every app can opt into governed AI capabilities (chat, summarize, classify) backed by Amazon Bedrock. Apps can only use generative AI through a controlled path with guardrails, quotas, and a full audit trail—never by embedding their own model keys. Apps that need elevated permissions (such as sending email or calling external domains) require an admin approval step backed by static code analysis and drift detection.
Amazon AI BlogRead Original Article

Get the latest Large Language Models news every morning

For example, today's edition would include:

  • DataAgent launches with $10M to auto-fix Kubernetes faultsSiliconANGLE AI · 1h ago
  • SK Hynix custom HBM boosts inference up to 5.15xDIGITIMES Asia · 1h ago
  • Nvidia Earnings: Boring by Design, Avoiding a Consolidated WorldStratechery (Ben Thompson) · 1h ago

AI-summarized, only the topics you pick — one digest a day via Email, Slack, or Discord.

Free · takes 30 seconds · unsubscribe anytimeWhat is AIToday? →

Ask AI

Ask AI anything about this article. Q&As are published on this page for other readers too.

Related Articles

Next articleU.S., China race for humanoid robot dominance