Devesh JoshiCo-founder, product
Nine years building AI platforms serving 12,000+ engineers. LLM platforms, agentic systems (MCP), and multi-model safety evaluation.
Reliable agentic workflows: Tool grounding and JSON schema validation
Why autonomous AI agents fail in production environments and how strict JSON schema enforcement, deterministic fallback layers, and zero-trust permissions prevent runaway execution.
Why unstructured prompts fail in production
Relying on free-text LLM prompts to invoke backend APIs leads to hallucinated parameter names, malformed JSON, and unpredictable execution paths. In enterprise automation, an agent that fails 5% of the time is unusable.
Enforcing strict schema contracts
Bind every external tool (CRM lookups, database queries, webhooks) to strict OpenAI function calling or Pydantic JSON schemas. Require the model to output validated JSON arguments before any server code executes.
Validate arguments against a middleware schema validator (e.g. Zod or JSONSchema). If validation fails, return the exact validation error back to the model context for an immediate, self-correcting retry loop.
Zero-trust tool execution boundaries
Never grant an AI agent direct write access to production databases without rate limits and permission scopes. Treat agentic tool calls as unauthenticated user input: validate, sanitize, and log every payload in an audit log.
Topic Focus & Target Concepts
This is the work behind our AI and automation practice — agents with real grounding, voice intake, and retrieval that answers from your records rather than the model's training data.
Agents with real tool accessRunning into this in your own stack? Twenty minutes, no deck.
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