These are practical build patterns, not prompt collections. Each guide explains the architecture, shows a small reference implementation, names the failure modes, and tells you what still needs proving before production.
Start with something real.
An existing service, workflow or product surface.
Add one AI capability.
MCP, handoffs, grounding, automation or shared admin operations.
Keep the boundary visible.
Permissions, provenance, approvals and failure states stay explicit.
Free to read. No email required. Draft guides are labelled as drafts; production claims are separated from examples.
Pick the problem you actually have. The detailed guide contains the implementation pattern and reference code.
MCP integration
Expose a small, intentional set of capabilities from a product or service through MCP without turning the whole backend into an agent API.
Use it when: You already have useful data or actions and want Claude, ChatGPT, Cursor or another agent client to call them safely.
Read the implementation guideAgent handoffs
Use a shared artifact and explicit handoff contract so one assistant can produce work and another can review or continue it without losing the brief.
Use it when: You want multiple models in one workflow without building an opaque multi-agent swarm.
Read the implementation guideBounded automation
Turn an approved queue into a repeatable set of internal drafts and checks while keeping consequential external actions with a person.
Use it when: There is recurring operational work worth automating, but you still need clear human control and auditability.
Read the implementation guideLive context with Pointmoon
Give an agent sourced, time-stamped environmental context and teach it to preserve freshness, provenance and explicit unknowns.
Use it when: Your AI needs to reason about weather, place, season or other changing physical-world conditions.
Read the implementation guideAI-native operations
Design one controlled operation that both a human interface and an assistant can use: read, propose, preview, validate, approve, commit.
Use it when: You want agents to help operate your product without giving them direct database access or bypassing existing controls.
Read the implementation guideThe existing library also covers grounded LLM generation, prompt operations, shared agent memory, voice AI pipelines and operating multiple agent workers. Those guides carry their own maturity labels and evidence notes.
Implementation support
We can adapt one of these patterns to your product, connect it to your existing systems, add the missing production controls, and leave your team with a working implementation rather than a prototype nobody owns.
Tell us what you are trying to build