Viewpoint
What separates an agent that survives production from a demo.
Almost every agent demos well. The ones that last share four traits, and none of them are about the model.
Drawn from production AI systems Alex has shipped, and the ones that broke on the way there.
02 / What an agent should be
Plan, act, recover. Not autocomplete with a logo.
Planner
Decomposes a request into steps. Reasons over your domain, your tools, and your data.
Tool layer
MCP servers, REST clients, database adapters. Tools have schemas, timeouts, and audit logs.
Memory
Short-term scratch, long-term in Postgres with RLS. No leaking state across users.
Eval harness
Golden tasks, regressions, model upgrade guards. You see quality before customers do.
03 / Stack
Claude. MCP. Postgres. TypeScript.
Anthropic Claude across API, Bedrock, and Vertex. MCP for tool surfaces. Postgres with Row Level Security for state and permissions. TypeScript end to end. n8n when orchestration is the bottleneck. Helicone, OpenTelemetry, or Langfuse for observability.
04 / Multi-agent when it earns it
Hierarchies only after a single agent works.
ClawKeeper runs 110 agents in a deterministic finance hierarchy. CipherClaw debugs multi-agent systems in production. Both started as single agents that earned the right to spawn more.
05 / Proof
Shipped systems, not slideware.
Completed 9 Anthropic Academy courses (Agent Engineering, Claude Code, MCP, Context Engineering). Lovable Champion and community leader. 12+ live AI products with auth, billing, and customers. Open-source agent tooling on GitHub.
FAQ
Common questions.
- What is a custom AI agent?
- A program that uses an LLM to plan, call tools, and act on real systems with guardrails, retries, and observability. Not a chatbot.
- Single agent or multi-agent?
- Start single. Add agents only when role separation reduces error rate or cost. Most teams ship value with one well-built agent before they need a hierarchy.
- Which models do you use?
- Claude (Anthropic) as default. OpenAI when tool-use or function quality demands it. Local or open-weight models on request.
- How do you keep agents from going off the rails?
- Evals before launch, guardrails at tool boundaries, Postgres RLS for data access, full logging, and kill switches. Production AI runs on receipts.
- How do permissions work?
- Agents act as scoped service accounts. Sensitive actions require human approval. Row Level Security in the database enforces who can read or write what.
- Can this integrate with an existing stack?
- Usually. MCP for tool exposure, REST or GraphQL where it already exists, webhooks in both directions. Migration is rarely the prerequisite people expect.
- Where does the paid work live?
- On TechTide AI. This page is the reasoning, not an offer.
Have an agent that almost ships?
Hardening an agent into something your team can own runs through Systems Decision Audit at TechTide AI, where the scope and the pricing live.