Technical Strategies & Architecture

Audience: Senior Tech Leads • CTOs • AI/MLOps Engineers • Product Leaders
Promise: Ship the smallest agent that solves the job — and make it safe, observable, and governable.

Sidebar TOC

TL;DR

• Start with the minimum autonomy that solves the job.
• Treat tools as product: strict contracts, permissions, budgets, idempotency.
• Add guardrails + HITL by default for high-impact actions.
• Ship with tracing + evals like you ship with tests + monitoring.
• Build an explicit failure → replay → regression loop.
• Govern rollouts with feature flags + canaries + kill switches.

Should this be an “agent”?

Use an agent when the job requires multi-step decisions + tool use + iteration, not “one prompt → one response.”

The 3-box filter (agent-ready use case)

A use case is agent-ready only if it is:

• Valuable (measurable ROI: time, cost, risk, revenue)
• Executable (tools/data exist and can be accessed safely)
• Governable (you can bound impact + audit actions)

If any box fails: start with deterministic workflows, automation, or a single-shot assistant.

Agent stack (4 primitives)

Agents are engineered from four primitives:

1. Instructions — what “good” means (testable, structured)
2. Tools — how it acts (APIs, DBs, file ops, MCP)
3. Memory/Data — what it knows (session, RAG, long-term)
4. Orchestration — how it plans/loops (routing, planners, subagents)

Playbook cards (Key Principles)

Core mental models

• Autonomy ladder: Start simple; earn autonomy via eval evidence.
• Four primitives: Instructions • Tools • Memory • Orchestration.
• Glass-box agents: Traces + metrics + replayability from day 1.

Reliability & safety

• Layered guardrails: Input → tool gating → output validation → stop rules.
• Tool contracts: Typed IO, strict parsing, budgets, idempotency, rollback.
• Evals as a pipeline: CI → staging → prod monitoring; failures become tests.

Governance & rollout

• Least privilege: Deny-by-default tools, role-based capabilities, scoped creds.
• HITL approvals: Interrupt + resume for high-risk actions; clear escalation.
• Safe shipping: Flags → canaries → A/B → rollback → kill switch.

Orchestration ladder (start simple)

Rule: Don't build a multi-agent "society" until you've proven a single agent fails.

Use multi-agent when you need:

• specialization (domain experts)
• parallel research or parallel checks
• independent verification / debate
• strict permission boundaries by role

Avoid multi-agent when:

• the task is short and linear
• you lack evals/observability (you'll ship chaos faster)

Two stable patterns

1. Manager–Worker (manager decomposes, workers execute, manager synthesizes)
2. Handoffs (control transfers to specialist agents)

Tools are product (Tool Contracts + ACI)

If the agent can call it, you need a tool contract:

• strict schema (typed inputs/outputs)
• permissioning (least privilege)
• budgets (rate limits, token caps, cost caps)
• retries/timeouts + idempotency
• audit logs + rollback plan

ACI (Agent–Computer Interface) heuristics

• make tool names unambiguous
• embed examples + edge cases in tool descriptions
• return structured errors, not prose
• minimize "free-form" tool results

MCP integration (tool/data plane)

Use MCP when you have many tools × many agents and want:

• standard interfaces to tools/resources
• portability across runtimes
• centralized governance for tool access

Security posture:

• treat MCP servers as prod services (authn/authz, allowlists, logging)
• assume tool outputs can be malicious (prompt injection is real)
• validate everything at boundaries

Guardrails + Human-in-the-Loop (default stance)

Layer guardrails:

• Failure-resistant input: injection, policy, relevance checks
• Tool gating: allowlists + approvals for high-risk actions
• Output validation: PII/policy/format/schema checks
• Stop rules: loop caps, timeout, budget caps

HITL trigger policy (example)

Require approval when:

• action is irreversible (payments, deletes, emails)
• tool result indicates high uncertainty
• safety classifier flags elevated risk
• agent exceeds retry/loop thresholds

Observability: make it a glass box

Minimum instrumentation:

• traces: generations, tool calls, handoffs, guardrails decisions
• step-level latency + cost (tokens, tool runtime)
• outcome labels: success/failure + reason codes

Minimum trace schema

• run_id, agent_version, model_id, prompt_hash
• steps[]: tool_name, args_hash, status, retries, latency_ms
• totals: tokens_in/out, cost_estimate, p95 latency
• safety: flags, approvals, escalations

Evals: CI + staging + prod monitoring (AgentOps)

3-tier pipeline + failure→regression loop

Agents need scenario-driven, multi-metric evals.

3-tier pipeline

1. CI (every PR): fast unit + golden tests
2. Staging/canary: full suite + adversarial + rubric
3. Production: A/B for major changes + continuous monitoring

Failure modes & mitigations (what breaks + what to do)

Failure taxonomy + detect/constrain/prevent

Failure modes (common + expensive)

Detect (signals you can automate)

• Trace anomalies: loop count spikes, tool retry spikes, sudden tool mix changes
• Policy flags: injection indicators, sensitive data detectors
• Validators: JSON schema checks, unit checks, citation checks
• Canary alarms: error rate, p95 latency, cost/task, intervention rate

Constrain (make the blast radius small)

• Least privilege tool allowlists (per agent/subagent)
• High-risk tool approvals (interrupt + resume)
• Budgets: max steps, max tokens, max tool calls, max cost
• Sandbox tools: read-only mode, dry-run, staged writes
• Idempotency keys for side effects
• Structured outputs + strict parsing + rejection on failure

Prevent regression (make failures non-repeatable)

• add every incident as a failure replay test case
• maintain adversarial eval packs (injection, jailbreak, data leakage)
• run evals in CI and block releases on key KPI regressions
• track deltas by agent_version + prompt_hash

Failure postmortem template

• Incident summary + impact
• Reproduction trace (run_id)
• Root cause: instructions / tools / memory / orchestration
• Mitigation shipped
• New regression tests added
• Rollback/kill-switch criteria updated

Governance posture (permissions, approvals, audit, rollout)

Governance checklist

Permissions model (capability-based)

• tool access is granted per role and environment (dev/staging/prod)
• separate read vs write tools (read-only by default)
• scoped credentials (time-bound tokens, per-tenant access)
• deny-by-default + explicit allowlists

Approvals & escalation

• define risk tiers: low/medium/high/critical
• enforce HITL for high-impact tools
• escalation routes: human reviewer → domain owner → security/compliance

Audit trails (non-negotiable)

Store immutable logs:

• prompts (or prompt hashes), tool args hashes
• tool results metadata (status, size, source)
• approval decisions (who/when/why)
• model + agent versions
• full trace IDs for replay

Rollout strategy (safe shipping)

• feature flags for progressive exposure
• canary releases on small traffic segments
• A/B tests for major behavior changes
• automated rollback when KPIs breach thresholds
• always-on “kill switch” for risky tools

Rollout checklist

• canary cohort defined
• KPIs + thresholds defined (quality, cost, safety, latency)
• rollback + kill switch tested
• audit logs verified
• incident on-call + playbook ready

Templates (copy/paste)

One-page Agent Spec (required for each workflow)

• Goal / non-goals
• Autonomy level (1–6)
• Tools allowed + risk ratings + approval policy
• Memory policy (none/session/RAG/long-term)
• Budgets (steps/tokens/tool calls/cost)
• Stop conditions
• Evals plan (CI/staging/prod)
• Observability plan (trace schema + dashboards)
• Rollout plan (flags/canary/kill switch)
• Ownership + on-call

Back to top ↑