AI Agent Resources
Technical guides, implementation frameworks, and field notes from shipping enterprise AI agents in production.
Featured
Start here for the strongest production-focused pieces.
All Resources
51 published articles.
Enterprise AI in 2026: How to Get From Experimentation to Production
Most organizations are not blocked by model quality. They are blocked by ownership, governance, testing, and the lack of a repeatable operating model.
MCP Roadmap: What Matters Now
As of March 25, 2026, the official MCP roadmap is less about hype and more about making production deployments scale, recover, authenticate, and govern cleanly.
Running OpenClaw in the Enterprise: A Practical Security Guide
Practical security guide for teams that want to use OpenClaw in enterprise environments. Network isolation, skill vetting, permission models, and deployment architecture.
AI Agent Observability: The Tools You Actually Need in Production
Most teams deploy AI agents and hope they work. Here are the observability tools, metrics, and alerting strategies you actually need to run agents in production.
How to Choose an AI Agent Framework
The right framework choice starts with the workload. TMA chooses frameworks based on retrieval needs, orchestration needs, operating environment, and how much abstraction the team should actually carry.
Sovereign AI: Why Enterprise AI Is Moving In-House in 2026
The enterprise case for sovereign AI is operational: control the data path, control the model path, and control the approval path.
Claude Opus vs GPT-5 for Production Agents
Frontier-model comparisons break down when they ignore the real workload. TMA evaluates Claude Opus-class and GPT-5-class models by task shape, not benchmark theater.
OpenClaw Security Risks: What Cisco Found (And What You Should Do About It)
Cisco's Talos team found data exfiltration and prompt injection vulnerabilities in OpenClaw's skill repository. Here's what they found and how to respond.
The Build vs. Buy Decision for Enterprise AI Agents (2026 Edition)
The real build-vs-buy decision is not ideology. It is a tradeoff between speed, control, maintenance, workflow fit, and how much custom infrastructure you actually want to own.
MCP Security: What Enterprises Need to Know Before Deploying
MCP security risks are real. Cisco's OpenClaw found critical vulnerabilities. Here's how to lock down MCP servers for enterprise production use.
AI Search Is Eating Traditional Search. Here's What That Means for Your Business.
AI search is changing how content gets discovered, but the useful lesson is not panic. It is learning what actually made TMA more crawlable, citable, and indexable.
AI Agent Architecture: The 4 Components Every Production System Needs
The four components every production AI agent needs: Perception, Reasoning, Action, and Memory. Architecture patterns, common mistakes, and implementation details.
State of AI Agents in 2026: The Data Behind the Hype
Forget the market-size theater. This is the state of AI agents in 2026 from the operator side: approvals, workflows, architecture, testing, monitoring, and maintenance.
AI Agent Maintenance: What Nobody Tells You About Life After Deploy
Deploying your AI agent is day one. Model drift, prompt degradation, API version changes, and cost creep are the real challenges nobody warns you about.
How to Test AI Agents Before They Touch Production Data
Most teams ship AI agents with a prayer instead of a test suite. Here are the testing strategies that actually catch failures before your customers do.
Telecom Leads AI Agent Adoption at 48%. Here's Why.
The useful telecom lesson is not the headline adoption number. It is why telecom workflows are such a clean fit for agents and what that tells other industries about where to start.
The AI Governance Gap
Most organizations are shipping AI faster than they can govern it. The fix is not more policy PDFs. It is visibility, ownership, enforcement, and reviewable operations.
When to Use AI Agents vs. Traditional Automation (Decision Framework)
Decision framework for AI agents vs. RPA. When to use each, when to use both, and how to avoid the most expensive mistake in automation.
How to Get Executive Buy-In for AI Agents
Executive approval rarely fails because the AI idea is too small. It fails because the proposal is too broad, too vague, or too risky to approve cleanly.
OpenClaw vs Claude Code vs ChatGPT
These tools solve different problems. The useful comparison is architecture, control, operational fit, and how much of each claim is directly validated in real work.
Fine-Tuning vs RAG
Fine-tuning and RAG solve different problems. TMA uses RAG for changing knowledge, fine-tuning for behavior and format, and hybrid patterns only when the extra complexity is justified.
AI Agents for Legal Teams: What To Automate, What To Review
Legal teams get value from agents when the work is document-heavy, source-grounded, and reviewable. The winning pattern is evidence-backed preparation, not blind autonomy.
Multi-Agent Systems: When One AI Agent Isn't Enough
Single-agent deployments are table stakes. The companies pulling ahead are running multi-agent systems -- coordinated teams of AI agents handling complex, cross-functional workflows. Here's how they're built, when they make sense, and what the architecture looks like.
AI Agents for Customer Service
Customer-service agents work when the workflow is bounded, the knowledge is grounded, and the escalation path is clean. They fail when teams ship a generic chatbot and call it transformation.
EU AI Act: Practical 2026 Guide
As of March 25, 2026, the EU AI Act is no longer a distant compliance topic. This is the practical timeline and readiness checklist enterprises should be using now.
Open-Source vs. Commercial LLMs: The Enterprise Decision Guide (2026)
The open-source versus commercial decision is not ideological. It is a workload design decision about speed, control, support, cost, and data boundaries.
What Is OpenClaw?
OpenClaw matters less because it is open source and more because it made the local-first, model-flexible personal-agent pattern legible to a much wider audience.
How to Build an MCP Server in Under an Hour (Step-by-Step)
Build a working MCP server in under an hour. Python and TypeScript examples, real code, common mistakes, and connecting to Claude or ChatGPT.
AI Agents in Healthcare: What Can Be Automated Safely
Healthcare can benefit from agents, but only when the architecture respects PHI boundaries, auditability, and human review. The useful question is what can be automated safely.
The One-Week AI Pilot: Our Exact Methodology for Shipping Fast
The actual TMA method for one-week pilots: choose the right workflow, define the hero metric, connect real systems fast, release with controls, and leave with a usable handoff package.
AI Agents for Back-Office Operations
Back-office agents usually win first because the workflow pain is obvious, the metrics are cleaner, and the approval path is easier than customer-facing AI theater.
Best Vector Databases for Production RAG
Most vector database choices should be made from workload shape, not benchmark screenshots. The right choice depends on search type, operations model, and where the rest of the stack already lives.
MCP vs. Traditional API Integration: Why Your AI Agent Architecture Needs to Change
MCP vs traditional API integration for AI agents. Comparison table, real cost numbers, and a practical migration path for enterprise teams.
AI Agents in Manufacturing: Where They Actually Fit
Manufacturing already has automation. The useful question is where an agent layer improves planning, exception handling, and decision speed without pretending it should replace plant control systems.
AI Agent Permissions: Why Least-Privilege Design Isn't Optional
Most AI agents ship with god-mode permissions. Here's how to design least-privilege permission models that prevent autonomous data movement disasters.
AI Agent ROI: How to Calculate It Before You Spend a Dollar
The best AI ROI calculations start with workflow pain and disciplined assumptions, not with vendor promises.
The CFO's Guide to AI Agent Investment (Spreadsheet Included)
The financial case for AI agents should be built like any other infrastructure decision: defined workflow, bounded risk, controlled assumptions, and a clear path from pilot to scale.
7 AI Agent Predictions for 2026 (Based on Data, Not Hype)
The useful 2026 predictions are the ones you can already see in production: tighter governance, more testing, better integrations, hybrid model stacks, and a bigger maintenance burden.
Gartner Says 40% of Apps Will Have AI Agents by 2026. Are You Ready?
The useful response to Gartner's embedded-agent prediction is not panic. It is preparing your workflows, permissions, testing, and governance for systems that can actually act.
Why AI Pilots Fail
Most AI pilots do not fail because the model was not smart enough. They fail because the workflow, metric, ownership, and release discipline were weak from the start.
Claude vs GPT-4 for Enterprise Agents
The right model choice comes from real workflow evaluation, not benchmark screenshots. TMA routes Claude and GPT-4-class models differently based on the job.
Why OpenClaw Went Viral (And What It Means for Enterprise AI)
The useful lesson from OpenClaw is not the hype cycle. It is what users clearly want: local control, flexible models, direct interfaces, and fewer platform bottlenecks.
What Is MCP? The Model Context Protocol Explained for Enterprise Teams
MCP is the open standard for connecting AI agents to external tools and data sources. 97M monthly SDK downloads. Here's the definitive enterprise explainer.
The Enterprise AI Security Checklist You're Probably Ignoring
A 20-point actionable security checklist for enterprise AI deployments. Covers data classification, access controls, audit trails, model permissions, and vendor assessment.
AI Agents in Financial Services: What Actually Ships
Financial-services teams get the most value when agents prepare, prioritize, document, and route work inside a controlled environment. The strongest deployments do not skip governance.
The Real Cost of Building AI Agents In-House (It's Not What You Think)
The hidden cost of in-house AI builds is not only coding. It is the full operating burden that appears before launch and keeps growing after launch.
Data Sovereignty for AI Agents
Data sovereignty is not a slogan. It is a design decision about where the data path, model path, and control path should live for each workflow.
LangChain vs LlamaIndex vs CrewAI
The right framework choice starts with the job. TMA uses LangChain, LlamaIndex, CrewAI, and direct APIs differently because they solve different infrastructure problems.
AI Agents vs Chatbots
The useful difference is simple: chatbots answer, agents operate. The buying mistake happens when teams buy a response layer for a workflow that really needs controlled action.
How to Build Your First AI Agent (Without Losing 6 Months)
Most teams spend 6-12 months building their first AI agent and still fail. Here's the step-by-step approach that actually ships production agents in weeks.
How to Ship AI Pilots in 1 Week (While Consultants Take 90 Days)
How TMA ships AI pilots in one week: what qualifies, what does not, what gets delivered, and why speed only works when scope and controls are both tight.