Understand the agent stack before you build on it.
A research layer for builders: compare tools, study architectures, understand security boundaries, follow production case studies, and track what is changing across AI agents.
Agent Comparisons
Compare coding agents, models, platforms, and agent tooling around real builder decisions.
Open deskAgent Security
Security patterns for permissions, credentials, prompt injection, tool access, isolation, and approvals.
Open deskAgent Architecture
Practical architecture patterns for tool-calling loops, memory, orchestration, APIs, MCP, and observability.
Open deskAgent Tools
Explore the infrastructure agents use: coding environments, APIs, MCP servers, databases, browsers, and automation.
Open deskAgent Case Studies
Real-world agent implementations, product launches, architecture decisions, failures, and lessons.
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The latest AI agent launches, model changes, infrastructure moves, funding, and ecosystem shifts.
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Latest agent research
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MCP Client Architecture: How AI Agents Discover and Use Tools
MCP clients connect agent runtimes to external capabilities, but discovery does not equal authorization.
6 min readMCP Security for AI Agents: How to Review Tools Before Connecting Them
Connecting an agent to an MCP server expands its capabilities and its attack surface. Review tools, permissions, trust boundaries, and outputs before enabling them.
6 min readThe New Developer Tooling Layer: Security for AI Agents
As coding agents gain access to repositories and external tools, security around permissions, provenance, and safe execution is becoming a new product category.
5 min readMeta Muse Hits Amazon’s Wall: What Agentic Commerce Means for Solo Founders
Amazon blocked Meta’s viral Muse agent from shopping. Indie teams now need explicit agent identity, opt-in APIs, and fallback paths—not silent browser automation.
9 min readAI Agent Tracing: How to Design End-to-End Agent Traces
A useful trace should explain what an agent did, why it did it, and where the workflow spent time.
6 min readAI Agent Session Replay: How to Debug Multi-Step Agent Runs
Multi-step agent failures are difficult to reproduce because the final error often hides the earlier decision that caused it.
6 min readAI Agent Production Dashboards: What to Monitor After Launch
A production dashboard should answer whether agents are working, becoming expensive, getting slower, or creating risk.
6 min readAI Agent Latency Monitoring: How to Find Slow Agent Workflows
Agent latency is distributed across model calls, tools, queues, databases, and external APIs.
6 min readAI Agent Failure Classification: How to Debug Production Runs
Not every failed agent run has the same cause.
6 min readAI Agent Evaluation Metrics: How to Measure Production Quality
Successful HTTP requests do not prove that an agent completed the right task.
6 min readAI Agent Cost Observability: How to Attribute Spending Per Workflow
Agent cost is difficult to control when model calls and external services are not tied to individual workflows.
6 min readAI Agent Alerting: How to Build Useful Production Alerts
Alerting every time an agent fails creates noise and teaches teams to ignore monitoring.
6 min readOAuth vs API Keys for AI Agents: Which Should Developers Use?
A practical comparison of API keys, OAuth, and service identities for AI agents.
8 min readAI Agent Token Exchange: How to Issue Scoped Credentials to Agents
Agents often need temporary access to downstream systems without receiving a user's permanent credential.
6 min readAI Agent Reliability: Retries, Timeouts, Fallbacks and Human Review
Reliable agents use bounded retries, explicit timeouts, fallbacks, idempotent writes, and human review for uncertain or high-impact operations.
6 min readAI Agent Costs: How to Calculate the Real Cost Per Task
Agent cost is more than token price. Count model calls, tool calls, retries, sandbox time, and human correction against completed outcomes.
6 min readAI Agent Step-Up Authentication: When Actions Need Extra Verification
Not every agent action deserves the same authentication strength.
6 min readAI Agent Error Handling: What Happens When the Model Fails?
Treat model failures as normal software states. Classify them, retry only when useful, and give the workflow a terminal failure state.
6 min read