The OguzHub Blog
Notes from building AI agents.
Engineering write-ups for people shipping LLM-powered software — agents, tool use, retrieval, evals, guardrails, and getting it to production.
Multi-agent orchestration patterns for production systems
Concrete patterns for routing work across agents, handling failures, and maintaining observability. Real tradeoffs between centralized control, choreography, and hybrid approaches.
More from the journal
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LLM APIs
Handling LLM rate limits and retries in production
How to build retry logic that doesn't amplify failures, when to use exponential backoff versus queuing, and why naive retry loops break under load.
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LLM Integration
Getting structured output from LLMs
How to reliably extract JSON, enums, and typed data from LLM responses. Covers schema enforcement, parsing strategies, and production failure modes.
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AI Agents
Adding guardrails to AI agents in production
Concrete patterns for preventing bad outputs, tool misuse, and cost overruns. What actually works and what fails in practice.
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LLM Agents
Building AI agents on Amazon Bedrock
How to wire tool use, manage state, and handle failures when building agents with Bedrock's API. The tradeoffs you'll actually face in production.
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LLM Reliability
Reducing LLM hallucinations in production systems
Concrete strategies to catch and prevent false outputs in agent systems. Covers grounding, verification layers, and the tradeoffs of each approach.
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Security
Defending against prompt injection in production
Prompt injection is real but solvable. We walk through the architectural patterns that actually work, what fails, and the tradeoffs you'll face shipping agents to production.
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LLM Evaluation
How to evaluate LLM outputs in production
Building evals that catch real failures before they reach users. Concrete patterns for scoring, thresholding, and routing broken outputs.
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Agents
RAG vs fine-tuning for agents, and when to combine both
Fine-tuning and RAG solve different problems in agent systems. Here's how to choose, what each costs you, and why you probably need both.
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Tool Use
How to add tool use to an LLM
The mechanics of function calling, parsing, and execution. What breaks, how to handle it, and what you actually need to build.
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Agent Architecture
LLM agent architecture patterns for production
Concrete patterns for building reliable agents: routing, planning, tool orchestration, and state management. Tradeoffs and failure modes you'll actually encounter.
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AI Agents
How to build an AI agent that actually works
The shape of a real agent system—state management, tool calling, failure handling, and the tradeoffs that matter when you ship to production.
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Go To Market
Integrations close deals, not your novel algorithm
Buyers don't pay for clever internals. They pay to make your product disappear into their stack — identity, data, and workflow. Ship the connectors and your conversion moves.
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Enterprise
Enterprise buyers pick the AI agent that can prove it's right
In enterprise deals, the agent that can demonstrate provenance, reproducibility, and policy fit beats the one that feels smarter. Build the evidence pipeline, not just the brain.
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Security
Seven ways a document can hijack your AI agent
Prompt injection isn't a jailbreak you can patch. It's a consequence of asking a model to both read untrusted text and decide what to do about it — and the fix is architectural, not a longer system prompt.