AI Agents
20 articles
Optimizing agent cost and token usage
Identify where tokens go, right-size model selection for each step, enable prompt caching, and set budget limits to keep your agent spending under control.
Agent security and data privacy
Store secrets securely, apply role-based access controls, minimize PII exposure in prompts, and produce the audit-log evidence trail for SOC 2 and GDPR compliance.
Multi-agent coordination and delegation
Design orchestrator-worker architectures, invoke specialist agents synchronously or asynchronously, and share state between agents using the Memory Store and Event Bus.
Agent versioning and rollback
Learn how Cotonity snapshots every published version, compare diffs between any two versions, roll back to a stable release in seconds, and maintain clear release notes.
Updating a live agent without downtime
Edit a published agent safely by working in draft, understand graceful handover for in-flight runs, apply low-risk vs. high-risk changes, and coordinate schema changes with upstream systems.
Monitoring agent performance metrics
Understand the agent metrics dashboard, drill into per-step bottlenecks, configure threshold alerts, and export telemetry to Datadog, Grafana, or any OTLP backend.
Deploying agents to production
Follow the pre-deployment checklist, promote a draft to live, use traffic splitting for zero-downtime rollouts, and monitor the first real-world executions.
Testing your agent in sandbox mode
Run agents safely without side-effects, supply realistic test inputs, step through execution node by node, and build a repeatable test suite before every deployment.
Conditional logic and branching
Add Branch nodes for if/else routing, use LLM-driven classification for nuanced decisions, merge paths back together, and keep complex canvases readable.
Passing data between agent steps
Master the expression system, built-in transformation functions, the Set Variables node, and the Run Inspector to build and debug clean data pipelines between steps.
Using LLM prompts within agents
Design effective system prompts, inject dynamic data, enable structured output parsing, and choose the right model for each step to optimize cost and quality.
Agent error handling and retries
Configure per-node retry policies, build a global error handler, ensure idempotent operations, and replay failed runs from the exact step that broke.
Connecting agents to external APIs
Use the HTTP Request action to call any REST API — handle auth, parse responses, manage rate limits, and implement back-off strategies for robust integrations.
Multi-step agent workflows
Build sequential pipelines, parallel branches, and loops — and handle errors gracefully so a single step failure doesn't halt your entire automation.
Understanding agent memory and context
Explore how agents remember information within a run and across runs — ephemeral context, key-value memory stores, and vector memory for semantic retrieval.
Setting up agent actions and tools
Configure built-in actions, connect third-party integrations, register custom tools via OpenAPI, and control which tools your LLM can call autonomously.
Configuring agent triggers — webhooks, schedule, and manual
Set up every trigger type Cotonity supports — webhook endpoints, cron schedules, manual runs, and how to combine multiple triggers on a single agent.
Designing an agent with the visual editor
Master the Cotonity visual canvas — adding and connecting nodes, mapping data between steps, saving drafts, and organizing your agent graph for long-term maintainability.
Types of agents — reactive vs. autonomous
Understand the spectrum from simple reactive agents to fully autonomous ones, and learn how to choose and combine agent types for your specific use case.
What is an AI agent and how does it work
A conceptual introduction to AI agents — how the perceive–reason–act loop works, when to use agents over simpler automation, and what makes Cotonity agents unique.