End-to-end roadmap to evolve a plain LLM chatbot into a production-grade agentic AI system. It’s organized as maturity levels so you can climb one rung at a time without breaking things.
0 → 1: Robust Chatbot (baseline you can trust)
Goal: Deterministic, instrumented, secure LLM app.
Do:
- Spec the job: Narrow, measurable tasks + success criteria.
- Structured I/O: Force JSON outputs with schemas (pydantic/JSON Schema).
- Guardrails: Prompt templates, content filters, allow/deny lists.
- Telemetry: Log prompts, tool calls, tokens, lat/latency, errors, user satisfaction tags.
- Eval harness: Golden tests + regression suites (unit + scenario tests).
KPIs: response validity %, latency p95, hallucination rate, CSAT.
1 → 2: Tool-Using Assistant (from “talker” to “doer”)
Goal: LLM can call tools/APIs deterministically.
Do:
- Tool registry: Each tool has a contract: name, description, strict JSON args, auth scope, rate limits, cost.
- Router: Model chooses a tool via function-calling; you validate args before execution.
- Idempotency: Use request IDs + retries + timeouts.
- Sandboxing: Separate service accounts for read vs write tools.
KPIs: tool-call success %, invalid-arg rate, external error rate.
Tool contract example (concise):
{
“name”: “search_db”,
“description”: “SQL read-only over analytics”,
“schema”: {
“type”: “object”,
“properties”: { “query”: {“type”: “string”} },
“required”: [“query”],
“additionalProperties”: false
},
“auth_scope”: “analytics.read”,
“rate_limit_qps”: 5,
“timeout_ms”: 8000
}
2 → 3: Add Memory (short-term, long-term, episodic)
Goal: The assistant remembers context and learns across sessions.
Do:
- Memory layers:
- Working: conversation buffer (windowed).
- Episodic: “events” with outcomes & metadata.
- Semantic long-term: vector store for facts, docs.
- Profile/State: key–value store (preferences, configs).
- Write policy: What gets saved, for how long, and why (avoid hoarding).
- Read policy: Retrieval filters (freshness, scope, PII constraints).
- PII & compliance: Field-level encryption; TTLs; user “forget me”.
Minimal schemas:
episode:
id: uuid
ts: datetime
task: string
actions: [tool_name, args, result_ref]
outcome: success|failure|partial
notes: text
profile:
user_id: string
prefs: {key: value}
scopes: [string]
KPIs: retrieval hit rate, memory precision/recall (offline eval), PII leakage = 0.
3 → 4: Planning & Multi-Step Execution (the agentic loop)
Goal: Break big goals into actionable steps; recover from errors.
Plan–Act–Observe loop (core):
- Plan: LLM produces a structured plan (steps with tools/inputs/expected outputs).
- Act: Execute step N (validate + run tool).
- Observe: Capture results, update state/memory.
- Revise: Re-plan if blocked; stop when success criteria met.
Design choices:
- Shallow vs deep planning: Cap max steps; prefer re-planning over long initial plans.
- Critic model: Lightweight verifier scores step outputs (pass/fail/explain).
- Self-consistency: Re-ask the model for plan variants when confidence is low.
Task graph spec (example):
{
“goal”: “Produce a weekly sales summary slide”,
“constraints”: [“read-only db”, “finish < 3 mins”],
“steps”: [
{“id”:”s1″,”tool”:”query_sales”,”args”:{“week”:”2025-W35″}},
{“id”:”s2″,”tool”:”summarize”,”args”:{“input”:”$s1.rows”}},
{“id”:”s3″,”tool”:”make_slide”,”args”:{“markdown”:”$s2.text”}}
],
“success_check”:”slide has totals, trend, top 3 SKUs”
}
KPIs: task success rate, average steps per success, auto-recovery rate.
4 → 5: Multi-Agent Orchestration (specialists that collaborate)
Goal: Decompose roles (Researcher, Planner, Executor, Critic, Guard).
Do:
- Roles with charters: Each agent has tools & success metrics.
- Conversation protocol: Turn-taking with budgets (tokens/time/tool calls).
- Arbiter: A controller that stops loops, resolves conflicts, enforces costs.
KPIs: marginal gain vs single-agent baseline; overhead (tokens, latency).
5 → 6: Proactivity & Autonomy Controls
Goal: Let agents initiate work safely.
Do:
- Triggers: CRON, webhooks, event streams (“new lead added”, “SLA breach”).
- Policy engine (OPA/Rego or custom): who/what/when the agent may act.
- Approval ladder:
- Tier 0: read-only, always.
- Tier 1: low-risk writes, batch gated.
- Tier 2: high-risk writes, human review with diff.
- Budgeting: Per-goal caps (tokens, spend, API calls).
KPIs: % proactive actions accepted, budget adherence, incident rate (target ≈ 0).
6 → 7: Production Ops, Safety, and Governance
Goal: Treat your agent like a microservice—observable, auditable, upgradable.
Do:
- Observability: Tracing across plan→tool→result; redaction at log sinks.
- Risk controls: Rate limits, circuit breakers, kill switch, sandbox envs.
- Policy packs: Data residency, IP hygiene, model usage constraints.
- Model mgmt: Canary deploys, shadow tests, rollback, eval-gated releases.
- Human-in-the-loop: Review queues with structured feedback → fine-tuning/RLAIF.
KPIs: MTTR, change failure rate, eval pass rate pre-release.
Reference Implementation (minimal, production-leaning pseudocode)
class Tool(Protocol):
name: str
schema: Dict
def run(self, **kwargs) -> Dict: …
class Registry:
def __init__(self, tools: list[Tool]): self.tools = {t.name: t}
def validate(self, name, args): … # jsonschema.validate
def run(self, name, args): return self.tools[name].run(**args)
class Memory:
def read(self, query): …
def write_episode(self, episode): …
class AgentController:
def __init__(self, llm, registry: Registry, memory: Memory, critic):
…
def plan(self, goal, context) -> dict: …
def act(self, step): return self.registry.run(step[“tool”], step[“args”])
def loop(self, goal, context, budget):
plan = self.plan(goal, context)
for step in plan[“steps”][:budget[“max_steps”]]:
self.registry.validate(step[“tool”], step[“args”])
out = self.act(step)
ok, reason = self.critic.check(step, out)
self.memory.write_episode({…})
if not ok: plan = self.replan(goal, context, reason)
return self.finalize(plan)
Testing & Evaluation (make this non-negotiable)
- Offline: 50–200 canonical tasks with ground-truth; measure success & constraint violations.
- Simulated users: Synthetic conversations to probe edge cases.
- Chaos tests: Tool timeouts, 429s, malformed responses; verify recovery.
- Red-team: Prompt-injection, data exfiltration, jailbreaks, tool-misuse.
Automatic checks per run:
- “No-op tool spam” ≤ threshold
- External call budget not exceeded
- PII never leaves approved sinks
- All writes have diffs + approvals where required
Stack Suggestions (pick one from each row)
- Orchestration: LangChain | Semantic Kernel | Autogen | CrewAI
- Memory (semantic): pgvector/SQLite-Vec | Pinecone | Weaviate | Milvus
- State store: Postgres | Redis
- Eval: Ragas | E2E eval scripts + human review queues
- Policy: OPA/Rego | custom middleware
- Observability: OpenTelemetry + ELK/Grafana | vendor APM
- Models: Hosted LLMs (for reliability) + small local models for critics/parsers
Security & Compliance Quicklist
- Least-privilege API keys; rotate + scope.
- Output-to-action gap: show diffs for any write; require approvals per policy.
- Prompt-injection defenses: input sanitization, origin tagging, content sandboxing.
- Data handling: classify fields; encrypt at rest & in transit; region pinning.
- Third-party risk: vendor DPAs, SOC2/ISO evidence, incident SLAs.
Cost & Latency Controls
- Caching (prompt + retrieval) and distilled “fast path” models for simple steps.
- Early-exit heuristics and confidence-based reruns only when needed.
- Batch tool calls when safe; cap step counts; compress traces.
Migration Plan (pragmatic 6–8 weeks)
- Week 1–2: Instrument current chatbot; add eval harness + guardrails.
- Week 2–3: Introduce 2–3 high-value tools; ship read-only flows.
- Week 3–4: Add memory (episodic + semantic); ship retrieval-augmented tasks.
- Week 4–5: Implement plan–act–observe loop with critic + re-planning.
- Week 5–6: Limited autonomy with approvals; add proactive triggers.
- Ongoing: Multi-agent roles for complex domains; harden ops & governance.
Common Failure Modes (and fixes)
- Endless loops → step caps + critic + “no-progress” detector.
- Tool over-calling → per-tool budgets + cost-aware planner.
- Hallucinated tools/params → strict schema validation + tool name canonicalization.
- Memory hoarding → write policy + TTLs + size caps + periodic compaction.
- Prompt-injection → signed content sources, retrieval isolation, tool-call allowlists.