AI agents, managed like
a delivery team.
We use specialized agents for planning, engineering, research, content, and QA. Each sprint starts with a written contract and ends with separate evaluation plus human review.
The roster changes with the work. Access stays scoped, failures stay visible, and no builder approves its own output.
Our operating model
Clear roles. Testable work. A person at the gate.
We set the outcome, assign the right specialists, and make the review path explicit before execution starts. Small jobs may use one builder and an evaluator. Larger work can split across engineering, research, and content.
Typical roster
No single agent plans, builds, and approves the same task.
- 01
Scope the sprint
A human defines the outcome, working context, constraints, and approval boundaries.
- 02
Write the contract
The builder proposes a testable definition of done. A separate evaluator tightens it before work begins.
- 03
Build and evaluate
A specialist works inside the approved scope. The evaluator checks the result against the contract and returns failed work with specific findings.
- 04
Review the delivery
A person reviews the changes and supporting evidence, then decides what ships or needs another pass.
Framework field guide
Choose the runtime after the workflow
Frameworks matter, but they do not replace scope, evaluation, and review. Use these filters to narrow the options for your stack.
13 frameworks
LangGraph
by LangChainGraph-based agent workflows with state management, streaming, and human-in-the-loop support. Pairs with LangSmith for observability.
Rock-solid for complex multi-step workflows. The learning curve pays off at scale.
OpenClaw
by Peter Steinberger (OpenSource)Open-source agent runtime with workspace management, multi-channel messaging, reusable skills, browser and computer tools, and sub-agent orchestration.
We run our entire operation on it. It's what powers this page being written.
NemoClaw
by NVIDIA + OpenClawNVIDIA's enterprise security layer for OpenClaw. Adds sandboxed execution (OpenShell), network policy enforcement, filesystem isolation, and privacy-aware inference routing. Installs with a single command on top of any OpenClaw setup.
This is what makes OpenClaw viable for business. We deploy and manage NemoClaw for clients on Canadian infrastructure.
Dify
by LangGeniusFull visual platform for AI workflows. Drag-and-drop pipeline editor, built-in RAG, prompt management, and agent orchestration. Self-hostable.
Unbeatable for demos and MVPs. We use it to prototype before building custom.
OpenAI Agents SDK
by OpenAIThe production successor to Swarm. It focuses on agents, tools, handoffs, guardrails, tracing, and realtime voice, with official Python and TypeScript SDKs.
Great starting point. We often prototype here, then move to LangGraph or CrewAI as complexity grows.
CrewAI
by CrewAI IncRole-based agent teams. Define agents by role (researcher, writer, analyst), give them tools, and let them collaborate.
Fastest time-to-demo for clients. The role metaphor clicks immediately in business conversations.
Google ADK
by GoogleGoogle's agent framework with native Vertex AI integration. Supports Gemini and 20+ other models via LiteLLM. Rich tool ecosystem including MCP.
A strong fit for Google Cloud teams. Its support for MCP, third-party tools, and agents-as-tools makes mixed systems practical.
Microsoft Agent Framework
by MicrosoftMicrosoft's production framework for .NET and Python agents, multi-agent workflows, hosted agents, observability, and reusable skills. Version 1.0 became generally available in April 2026.
The current choice for Microsoft shops. Do not start a new project on the older AutoGen repository without checking the migration path.
smolagents
by HuggingFaceAgents that think in code. Minimal abstractions, very Pythonic. If your team lives in notebooks and prefers writing code over configuring YAML, this is it.
Delightfully simple. The 'agents write code' paradigm feels natural for technical users.
Mastra
by Mastra AIThe TypeScript-native agent framework. First-class option if your backend is Node.js and you don't want to maintain a Python service just for agents.
Refreshing to see a first-class TS framework. The ecosystem is smaller but growing fast.
Codex CLI
by OpenAIOpen-source terminal coding agent from OpenAI. Sandboxed execution, multi-model support, and deep integration with ChatGPT and the Codex cloud platform. Think of it as the local counterpart to Codex Web.
A mature coding agent with local, cloud, and multi-agent workflows. Pair it with the Agents SDK when the workflow extends beyond software delivery.
Claude Agent SDK
by AnthropicThe agent loop and tools behind Claude Code, packaged for Python and TypeScript applications. It includes permissions, sessions, MCP, sub-agents, and structured output.
A practical choice when the job resembles Claude Code: long-running work, explicit permissions, and rich tool access.
Pydantic AI
by PydanticTyped Python framework for agents, tools, durable graphs, evaluations, and model portability. It uses Pydantic models to validate inputs and structured outputs.
One of the cleanest code-first options for business systems where validated outputs matter more than agent theatre.
Managed Platforms & Services
Paid platforms where someone else handles the infrastructure. Ship agents without managing servers.
11 platforms
Lindy
by Lindy AIPersonal AI work assistant you text via iMessage or SMS. Autonomously manages your inbox, drafts replies in your voice, handles meeting scheduling and note-taking across hundreds of integrations.
A strong personal-agent option for professionals who spend most of the day in email and meetings.
Devin
by Cognition AIAutonomous AI software engineer that reads codebases, writes code, runs tests, debugs, and ships PRs. It works asynchronously in sandboxed environments.
Still the most serious autonomous coding agent in production; the price drop from $500/mo made it worth trying for any team.
Relevance AI
by Relevance AIBuild and deploy AI agent teams using a low-code visual builder. Agents use tools, browse the web, call APIs, and work together on sales, support, and ops workflows.
One of the most production-ready agent platforms for non-engineers; the team-of-agents model is genuinely novel.
Voiceflow
by VoiceflowDesign, build, and deploy customer-facing AI agents across chat, voice, and web. Balances deterministic conversation flows with agentic playbooks.
A credible production option for customer experience agents, with real enterprise deployments behind it.
CrewAI AMP
by CrewAIManaged cloud platform on top of the open-source CrewAI framework. Adds a visual editor, agent lifecycle management, tracing, guardrails, and enterprise deployment.
The cleanest bridge from open-source experimentation to real production.
Retell AI
by Retell AIBuild and deploy AI voice agents for inbound and outbound phone calls. Handles customer service, appointment scheduling, lead qualification with real-time speech and interruption handling.
Best-in-class voice agent platform right now; the per-minute pricing model is refreshingly transparent.
Sierra
by Sierra AIAI customer experience agents for large enterprises. Outcome-based pricing tied to resolved conversations rather than compute. Used by major consumer brands for tier-1 support.
Outcome-based pricing is the right model for this category; you only pay for success.
Agentforce
by SalesforceSalesforce's native AI agent platform. It deploys autonomous agents across sales, service, marketing, and commerce, grounded in CRM data.
The most credible enterprise agent play because it has real data access.
Gumloop
by GumloopNo-code AI automation platform with a visual node-based editor. Connect tools, LLMs, and subflows to build agents for marketing, data, and ops workflows.
The best Zapier replacement for teams that actually want AI doing the thinking, not just the routing.
Stack AI
by Stack AIEnterprise AI agent builder with no-code and low-code interfaces. SOC 2, HIPAA, GDPR compliant with on-prem and VPC deployment options.
Not the sexiest UI but one of the few platforms that can genuinely say yes to a hospital's compliance checklist.
Codex
by OpenAIOpenAI's autonomous coding agent. Takes tasks end-to-end in a cloud sandbox: reads codebases, writes code, runs tests, ships PRs. Available via web, desktop apps, and CLI. Multi-agent coordination for larger projects.
A polished option for cloud and local coding work, especially for teams already using ChatGPT and the Codex CLI.
Persona & Skill Libraries
Pre-built agent personas and design skills. Don't start from scratch.
Agency Agents
51 personas51 pre-built agent personas covering frontend, backend, security, growth, community, and other roles. The SOUL.md files work with OpenClaw, Claude Code, and similar workspace-based tools.
Great starting point for persona design. Browse even if you don't use them directly.GitHub →
Impeccable
18 commandsFrontend design skill for AI coding tools. Commands like /distill (simplify UI), /colorize (brand colors), /animate, and /delight. Makes vibe-coded UIs look intentional instead of generic.
Install this if you're using Claude Code or Cursor for frontend work. The difference is noticeable.GitHub →
Google ADK Skills
GoogleDevelopment skills covering APIs, coding patterns, deployment, and evaluation. Works with Gemini CLI, Claude Code, and Cursor.
Useful reference for structuring your own dev skills.GitHub →
Testing, Security & Memory
The tooling that keeps agents from going off the rails.
PromptFoo
Unit testing and red teaming for LLM prompts and agents. Compare prompts across models, run automated security scans, and integrate with CI/CD. PromptFoo agreed to join OpenAI in March 2026, and the open-source project continues.
npx promptfoo@latest initEvery production AI app should run PromptFoo before shipping. We use it for our own agents.
OpenViking
ByteDance's context database for agents. Tiered loading (L0/L1/L2) dramatically reduces token consumption. Filesystem-based with auto-compression and self-evolving memory.
Watch this one. The tiered loading concept is the right direction for cost-conscious agent deployments.
Mem0
Memory layer for AI agents. Persistent memory across sessions, user preference tracking, and conversation history management.
Useful if your agent needs to remember users across conversations without building your own memory system.
Quick Picks
Tell us your situation. We'll tell you what to use.
GTA Labs · Toronto · Agent systems and delivery
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