#AI Safety

Articles about AI Safety — exploring patterns, best practices, and real-world implementations in production systems.

8 posts tagged with ai safety. ← All posts

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Pratik Dhanave · ·14 min read

Guardrails and Content Safety

The defensive layer that screens what goes into a model and what comes out — input rails, output rails, topical rails, and groundedness checks — plus the real tooling ecosystem and a vendor-neutral Python pipeline that wraps a model call and knows how to refuse.

The defensive layer that screens inputs and outputs: input/output/topical/groundedness rails, the real ecosystem (Llama Guard, Granite Guardian, NeMo Guardrails, Guardrails AI, Presidio, hosted moderation), and a vendor-neutral guardrail pipeline — with honest false-positive/negative trade-offs.

Pratik Dhanave · ·11 min read

Guardrails and Granite Guardian

Adding safety to a watsonx app in Python with two complementary layers — the built-in HAP and PII moderations that watsonx.ai applies to generation, and IBM's dedicated Granite Guardian risk-detection models run as classifiers around your main model to catch harm, jailbreaks, and RAG hallucination before a response reaches a user.

Add safety to a watsonx app in Python with two layers: built-in HAP/PII moderations on generation, and Granite Guardian — IBM's dedicated risk models — run as a classifier to screen input and check RAG groundedness, wrapped in a guarded-generate flow.

Pratik Dhanave · ·11 min read

Guardrails with NeMo Guardrails

Adding a safety layer to an NVIDIA-stack Python app with NeMo Guardrails — and why running it in-process, with no HTTP boundary, is the quiet advantage Python gives you over a separate guardrails server.

Add safety with NeMo Guardrails, in-process in Python: the rail types (input/output/dialog/retrieval), the config.yml + Colang model, loading with RailsConfig/LLMRails backed by a NIM model, and detecting when a rail trips instead of treating a refusal as a normal answer.

Pratik Dhanave · ·12 min read

Guardrails and Safety

How to put Amazon Bedrock Guardrails in front of a model from Go — attaching one to a Converse call, screening raw text with ApplyGuardrail, and reading whether the guardrail actually intervened.

Guardrails for Amazon Bedrock in Go: content filters, denied topics, PII/sensitive-information filters, and contextual grounding — attaching a guardrail to a Converse call and screening arbitrary text with ApplyGuardrail, checking for intervention.

Pratik Dhanave · ·8 min read

Limits and the Frontier

Reasoning models are a genuine advance — and it's precisely because they're so impressive that their limits are worth stating plainly. A model that writes out careful, step-by-step reasoning invites you to trust the reasoning, and that trust is not always warranted. The chain of thought may not be why the model actually answered. More thinking eventually stops helping. And some problems no amount of test-time compute will solve. Knowing the edges is part of using the capability wisely.

Reasoning models are a genuine advance — and precisely because they're so impressive, their limits are worth stating plainly. The chain of thought may not be why the model actually answered. More thinking eventually stops helping. And some problems no amount of test-time compute will solve.

Pratik Dhanave · ·3 min read

Observability, Safety, and Providers — Microsoft Agent Framework in Go

Wrap every run in an OpenTelemetry span, gate risky tool actions behind a permission handler, and swap model providers behind one agent.Agent.

Wrap every Microsoft Agent Framework run in an OpenTelemetry span, gate risky tool actions behind a permission handler, and swap Anthropic, OpenAI, Gemini, Copilot, and Azure behind one agent.

Pratik Dhanave · ·3 min read

Observability, Safety, and Providers — Microsoft Agent Framework in Python

Turn agent runs into OpenTelemetry spans, block prompt injection with information-flow control, and swap model providers behind one Agent API.

Turn Microsoft Agent Framework agent runs into OpenTelemetry spans, block prompt injection with information-flow control, and swap model providers behind one Agent API.

Pratik Dhanave · ·9 min read

The Future and Responsible AI in Energy

AI's potential to help operate a clean, complex grid is enormous — and precisely because the stakes are so high, this is exactly the domain where AI must be applied most carefully. A wrong forecast is one thing; an AI decision that destabilizes critical infrastructure serving millions is another entirely. The future of AI in energy is not "hand the grid to an AI" but something more disciplined and more valuable: AI as trustworthy decision-support that helps human operators run a decarbonized grid safely. This closing post is about that future, and the responsibility it demands.

AI's potential to help operate a clean, complex grid is enormous — and precisely because the stakes are so high, this is exactly where AI must be applied most carefully. The future is not 'hand the grid to an AI' but something more disciplined: AI as trustworthy decision-support that helps human operators run a decarbonized grid safely.

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.