Agent Evaluation: Task Success, Cost, Latency Agent systems can look impressive in a demo while failing quietly in production. The gap is not only model quality. It is evaluation discipline. A deployed agent is a workflow engine that reads, plans, calls tools, and produces outcomes under constraints. Evaluating an agent means evaluating the workflow, not […]

Agent Handoff Design: Clarity of Responsibility Handoffs are where agent systems either become trustworthy infrastructure or become a source of quiet risk. A handoff happens whenever responsibility moves from one actor to another: from agent to human, from agent to another service, from agent to a different role, or from one stage of a workflow […]

Agent Reliability: Verification Steps and Self-Checks Agents fail in ways that feel unfamiliar until you remember what an agent really is: a long-lived program that makes decisions, calls tools, accumulates state, and occasionally takes actions that cannot be undone. A single wrong step is rarely the full story. Most incidents come from small mismatches that […]

Conflict Resolution Between Agents Multi-agent systems are attractive because specialization can raise quality. One agent can focus on retrieval, another on planning, another on execution, another on verification. The risk is not only complexity. The risk is conflict: two agents propose incompatible actions, two agents interpret constraints differently, or two agents race to update the […]

Context Pruning and Relevance Maintenance Context pruning is how long-running agents stay relevant. Without pruning, context grows until it becomes expensive, slow, and misleading. The goal is to keep the minimal state needed to complete tasks while removing noise and outdated assumptions. Pruning Techniques | Technique | How It Works | Best For | |—|—|—| […]

Data Minimization and Least-Privilege Access Data minimization and least privilege are the two principles that keep AI systems from turning into accidental surveillance machines. Minimize what you collect and store. Restrict what the system can access and do. These controls protect users, reduce compliance burden, and shrink the blast radius of mistakes. The Principles in […]

Deterministic Modes for Critical Workflows Deterministic modes are essential when AI outputs must be reproducible: audits, compliance, financial workflows, and any system where inconsistent results cause operational damage. Determinism is not only temperature. It is the whole pipeline: prompt assembly, tool calls, retrieval, and validation. Sources of Non-Determinism | Source | Example | Mitigation | […]

Error Recovery: Resume Points and Compensating Actions Long workflows fail. Not because agents are careless, but because the real world is inconsistent. Inputs change. Tools return partial results. Permissions differ across environments. Dependencies stall. Even when every component is “mostly working,” the combined system can hit edge cases that stop progress. Recovery determines whether agentic […]

Exploration Modes for Discovery Tasks Exploration mode is the deliberate choice to trade determinism for discovery. When you are brainstorming, mapping an unfamiliar domain, or searching for creative options, diversity is valuable. The trick is to keep exploration safe: bounded budgets, clear outputs, and a path to converge on a decision. Exploration Versus Execution | […]

Guardrails: Policies, Constraints, Refusal Boundaries Guardrails are the constraints that keep an AI system aligned with its purpose under messy real-world inputs. A good guardrail strategy is layered: instruction constraints, tool constraints, output validation, and escalation paths. The goal is not to block everything. The goal is predictable behavior and safe failure modes. The Guardrail […]

Human-in-the-Loop Checkpoints and Approvals Human-in-the-loop checkpoints are how you combine automation with accountability. The best checkpoints are not random approvals. They are policy-driven gates placed at the exact points where the system could cause irreversible harm: external actions, sensitive data access, and high-stakes decisions. Done well, checkpoints improve trust without destroying usability. Where Checkpoints Belong […]

Interface Design for Agent Transparency and Trust Trust is a UX feature. If users cannot tell what an agent did, why it did it, and what sources it used, they will either over-trust it or refuse to use it. Transparency is not about exposing every token. It is about showing the right evidence and controls […]