Budget-Aware AI Agents: Keeping Cost, Tokens, and Latency Under Control
How to stop agents from looping forever, burning tokens, and slowing everything down.
Category: AI Agents
30 posts found
Page 2 of 3
Tracing AI Agents: Logging, Replay, and Debugging for Tool-Using Workflows
How to see what your agent actually did, step by step, and debug it like normal software. A practical guide to logging, replaying, and debugging AI agent 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.
Vector-Space Protocols — Building Shared Context Between AI Agents
How AI agents can communicate through shared vector spaces instead of brittle API schemas. Learn to build Vector-Space Protocols (VSPs) that enable semantic interoperability and emergent cooperation in multi-agent systems.
Function-Centric Agent Design: Moving Beyond Tool Use into Capability Composition
How function-centric architectures enable AI agents to dynamically discover, adapt, and chain capabilities into reusable graphs. Learn to build composable, observable, and self-optimizing agent systems that go beyond static tool invocation.
Trust-Aware Agents: Embedding Risk and Reliability into Decision Loops
How trust-aware agents make better decisions by evaluating reliability of data sources, peer agents, and tools. Learn to build trust scoring models, integrate trust into cognitive loops, and create systems that adapt based on reliability.