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App Corp
Full-service software engineering
Engineering your experience…
App Corp
Full-service software engineering
Engineering your experience…
Tool-calling agents, structured outputs, and production guardrails
App Corp builds production AI agents with TypeScript — tool-calling systems, structured outputs, multi-step workflows, and guardrails. Vercel AI SDK, OpenAI, and LangChain integration.
We build TypeScript AI agents using the Vercel AI SDK as the orchestration layer. The architecture:
Tool definitions specify what actions the agent can take — each tool has a name, description, parameter schema (Zod), and implementation function. The agent receives tool definitions, reasons about which tools to call, and executes them.
Structured outputs ensure the agent returns valid JSON matching a defined schema — not free-form text. This makes agent responses predictable and programmatically consumable.
Guardrails validate agent inputs and outputs at every step. We check for prompt injection, validate tool parameters, enforce rate limits, and log all agent decisions for debugging.
The agent loop follows a pattern: receive input → reason → select tool → execute tool → validate result → decide next action or return response. This loop runs until the agent determines it has enough information to respond.
Zod schemas validate tool parameters and outputs at runtime. Trade-off: schema design requires upfront effort but prevents runtime errors.
Forcing the agent to return valid JSON prevents many classes of errors. Trade-off: the agent cannot provide creative or unstructured responses.
Each tool call requires an LLM inference round-trip plus execution time. Trade-off: agents are slower than direct API calls but more flexible.
Input validation, output checking, and rate limiting add code. Trade-off: more code to maintain, but agents that fail safely rather than catastrophically.
LLM costs scale with agent complexity. A simple agent (2-3 tool calls per interaction) costs $0.01-0.05 per interaction using GPT-4-class models. Complex agents (10+ tool calls) can cost $0.10-0.50 per interaction. We mitigate costs by routing simple steps to cheaper models, caching tool results, and implementing max iteration limits. Monthly costs for a production agent serving 10K users typically range from $200-2,000 depending on complexity.
AI agents are App Corp's core competency. We have built production agents for EduPilotPro — 6 purpose-built agents handling attendance, admissions, fee collection, timetabling, performance analysis, and communication. Each agent achieved 91-96% accuracy on its specific domain, compared to 72% for a general-purpose agent.
Our TypeScript agent experience includes the Vercel AI SDK for orchestration, Zod for structured outputs, OpenAI and Google Gemini for inference, and custom guardrail systems for input/output validation. We have built agents that call external APIs, query databases, process documents, and trigger workflows.
The key insight from our experience: specialization beats generalization. One agent per domain, with tailored prompts, tools, and few-shot examples, outperforms a single general-purpose agent by 20+ percentage points.
Estimate architecture, cost, and ROI before you commit.
Deep-dives into architecture, cost, and implementation.
Real projects, real architecture, real outcomes.
Tell us what you want to automate. We will tell you whether an agent is the right approach, what architecture makes sense, and what it will cost — including the ongoing operating costs most people forget.