daemon-blockint-tech Agentic AI Developer
When to Use
- Implementing agent loops with tools: plan → act → observe → stop
- Designing tool/MCP schemas, auth, timeouts, and sandbox boundaries
- Building multi-agent workflows with routing, handoffs, and fan-out/fan-in
- Persisting agent state, checkpoints, thread memory, and resume semantics
- Adding human-in-the-loop approval, edit, or reject gates on risky tool calls
- Hardening agents: retries, idempotency keys, cancellation, and budget caps
- Instrumenting traces, spans, and trajectory logs for debugging and eval
- Running trajectory evals, golden sets, and regression gates before release
- Shipping agentic apps via API, queue workers, or durable workflow engines
When NOT to Use
- Training or fine-tuning foundation models, classical ML pipelines →
ai-engineer,ai-researcher - AI ops cadence, vendor contracts, rollout governance without implementation →
ai-lead-ops - Internal developer platform, golden paths, Backstage—no agent runtime →
platform-engineer - Generic service/API work with no agent loop, tools, or orchestration →
senior-software-engineer - Adversarial red-team campaigns and jailbreak harnesses only →
ai-redteam - Corporate AI policy, risk tiering, model cards without build →
ai-risk-governance - Pre-flight architecture or production-readiness review without building →
build-validator - Multi-agent system topology, routing, protocols, and fleet-level failure at architecture/engineering depth →
multi-agent-system-engineer - High-level multi-agent whiteboard without implementation → agent-designer (external skill; use conceptually)
Related skills
| Need | Skill |
|---|---|
| Broader LLM apps, RAG, model routing, cost/latency | ai-engineer |
| AI production ops, incidents, release gates | ai-lead-ops |
| Platform golden paths, IDP, developer portals | platform-engineer |
| Service design, APIs, code quality without agent focus | senior-software-engineer |
| Prompt injection, tool abuse, safety eval campaigns | ai-redteam |
| Governance, risk tiers, policy mapping | ai-risk-governance |
| Go/no-go plan or architecture validation | build-validator |
| Persistent memory stores and retrieval design | ai-memory-developer |
| Context packing and token budgeting | ai-context-engineer |
| Prompt templates and judge rubrics | prompt-engineer |
| Multi-agent system topology, routing, DAG, fleet observability | multi-agent-system-engineer |
| Multi-agent whiteboard without code (conceptual) | agent-designer (external) |
Core Workflows
1. Shape the agent runtime
- Define the user job, success metric, and stop conditions
- Choose runtime shape: single loop, supervisor + workers, or graph/DAG
- List tools/MCP servers; classify read vs write vs irreversible
- Set budgets: max steps, tokens, wall time, cost per session
- Decide checkpoint/resume and tenancy (thread_id, org_id)
See references/agentic_ai_developer_scope.md for scope boundaries and deliverables.
2. Implement loop + tools
receive task → plan (optional) → select tool → execute → observe → repeat | finalize
Checklist:
- Tool schemas are narrow; descriptions say when not to call
- Timeouts, retries, and idempotency on side effects
- Errors surfaced once to the model; no infinite retry loops
- Secrets never returned in tool results or traces
See references/agent_loop_tools_and_mcp.md for MCP and schema patterns.
3. Orchestrate multiple agents
- Assign roles: planner, executor, critic, specialist
- Handoff payload: goal, constraints, artifacts, open questions
- Avoid duplicate tool access unless idempotent; centralize dangerous tools
- Use fan-out/fan-in for parallel research; merge with structured reducer
See references/multi_agent_orchestration_and_handoffs.md for routing and handoff contracts. For system-level topology, fan-in policy, and cross-agent failure matrices, use multi-agent-system-engineer.
4. State, memory, and HITL
- Separate ephemeral scratchpad vs durable thread state vs long-term memory
- Checkpoint after each tool batch or subgraph node for resume
- HITL on tier-2+ actions: approve, edit args, or reject with reason
- Time out stalled human approvals; default-deny on expiry
See references/state_memory_and_hitl.md for checkpoint and approval patterns.
5. Reliability, observability, and evaluation
- Trace: session_id, span per model/tool step, redacted inputs/outputs
- Metrics: success rate, steps to completion, tool error rate, p95 latency, cost
- Eval: golden trajectories, tool-call correctness, task success (human or judge)
- Gate releases on regression suite; canary new prompts/graph versions
See references/reliability_observability_and_evaluation.md for eval and SLO patterns.
6. Security and production deployment
- Sandboxed tool execution; least-privilege credentials per tool
- Treat tool results and retrieved docs as untrusted input (injection aware)
- Deploy: sync API for short tasks; queue or durable workflow for long runs
- Kill switch, feature flags, and versioned prompts/graph definitions
See references/security_and_production_deployment.md for deployment topologies.
When to load references
| Topic | Reference |
|---|---|
| Role scope, deliverables, boundaries | references/agentic_ai_developer_scope.md |
| Agent loop, tools, MCP | references/agent_loop_tools_and_mcp.md |
| Multi-agent routing and handoffs | references/multi_agent_orchestration_and_handoffs.md |
| State, memory, checkpoints, HITL | references/state_memory_and_hitl.md |
| Retries, tracing, trajectory eval | references/reliability_observability_and_evaluation.md |
| Sandboxing, injection, deployment | references/security_and_production_deployment.md |
Framework pointers (optional)
Use framework docs for API specifics; this skill stays pattern-first:
| Pattern | Typical home |
|---|---|
| Stateful graph, interrupts, checkpointing | LangGraph-style graphs |
| Subagents, filesystem memory, HITL middleware | Deep Agents-style harness |
| Programmatic cloud/local agents, MCP in CI | Cursor SDK-style agents |
Do not duplicate full framework tutorials—implement the contracts above in the stack the team chose.