Hard Boundaries for AI Agents: Time, Budget, and Permission Controls in Production
How to stop agents from running wild: clear limits on time, tokens, tools, and user data. Practical patterns to keep AI agents under control in production systems.
Blog Posts
Page 6 of 17
Schema-First LLM Apps: Designing JSON and Tool Calls Before Prompts
How to treat LLMs as strict, structured components instead of free-form text generators. Start with schemas and tools, then write prompts around them.
Defending Against Prompt Injection: Building Secure LLM Pipelines
Learn how to design prompt pipelines that defend against adversarial inputs like prompt injection, malicious context, and out-of-distribution queries. Build production-ready LLM systems with proper input sanitisation, role separation, and monitoring.
Efficient Context-Window Management: Best Practices for Long-Form Retrieval-Augmented LLM Applications
Learn how to optimize context-window usage and retrieval-augmented generation pipelines when working with long documents. Covers chunking strategies, context budgeting, embedding retrieval, caching, and cost-performance trade-offs.
Observability & MLOps for LLMs: From Metrics to Anomaly Detection in Production
Production LLM systems need observability. Learn how to monitor prompts, track token usage, detect drift, catch hallucinations, and build alerting systems tailored for LLM workflows.
From Prompt-Chaining to Prompt-Graph: Next-gen Workflow Design for LLM-Powered Pipelines
Move beyond linear prompt chains to graph-based workflows. Learn how to design robust LLM pipelines with branching, error handling, and observability using prompt-graph architecture.
Adaptive Role Negotiation in Multi-Agent Systems
How autonomous AI agents can dynamically negotiate and adapt roles in real-time collaborations using LLM-driven role arbitration and reinforcement learning. Practical guide to building self-organizing agent teams.
Reflexive AI Agents: Closing the Loop Between Perception and Self-Evaluation
How AI agents can evaluate and adjust their own reasoning loops dynamically using internal reflection models. Practical guide to building agents that self-correct and adapt in real-time.
Memory Sharding for Scalable Agent Collectives
Building distributed cognitive memory for multi-agent ecosystems. Learn how memory sharding solves context collisions, slow retrieval, and redundant embeddings in large-scale agent systems.
Agentic Shadow Models — Reducing Latency via Local Predictive Replicas
How to build lightweight local replicas that predict remote agent responses to cut coordination latency in multi-agent workflows. Practical guide to shadow models with Python examples.