Navigate from initial assessment through production operations with a suggested roadmap. Each phase builds on the previous, establishing the foundation for agentic AI at scale.
Typical timeline: 6-9 months from assessment to scaled operations
Duration: 2-4 weeks | Pre-Launch Phase
Evaluate readiness, select use cases, and secure resources before committing to implementation.
Evaluate your organization across six critical dimensions. Score each dimension 1-5 to identify gaps and priorities.
Action: Focus on dimensions with largest gaps. Target minimum score of 3 across all dimensions before pilot launch.
Evaluate potential use cases across five dimensions to identify the optimal starting point. Higher combined scores indicate better pilot candidates.
Note: The scoring ranges below are illustrative examples. Adjust based on your organization's scale and context.
5: >$10M annual impact | 4: $5-10M | 3: $1-5M | 2: $500K-1M | 1: <$500K
5: All APIs ready | 4: Minor integration work | 3: Moderate complexity | 2: Significant challenges | 1: Major blockers
5: Minimal risk | 4: Low risk | 3: Moderate risk | 2: High risk | 1: Critical risk
5: Perfect team match | 4: Strong alignment | 3: Adequate skills | 2: Gaps exist | 1: Major gaps
5: <1 month | 4: 1-2 months | 3: 2-3 months | 2: 3-6 months | 1: >6 months
Pro tip: High business impact with moderate technical feasibility often beats perfect technical fit with low business value.
Use this matrix to determine appropriate autonomy levels based on stakes and reversibility:
High Stakes + Reversible: Semi-autonomous with audit trail
High Stakes + Irreversible: Explicit human approval required
Low Stakes + Any: Fully autonomous with audit data captured
Duration: 8-12 weeks | Initial Quarter
Build, test, and deploy a limited pilot to validate approach and demonstrate value.
Key Principle: Deploy a curated stack of models, not a single model for everything
Routing Layer (70% of queries): Fast classifier - Nova Micro at <10ms, $0.035/M tokens
Execution Layer (25% of queries): Balanced models - Nova Lite at $0.06/M tokens
Complex Tasks (5% of queries): Premium models - Claude or Nova Premier
Note: Token costs shown are illustrative examples. Actual costs vary by provider and model.
This pattern typically reduces costs by 60-80% while maintaining quality.
Match framework to your primary constraint:
LangGraph: Complex stateful workflows with branching logic. Production-proven at Klarna, Replit. Steeper learning curve but fine-grained control.
Strands (AWS): Advanced agentic topologies. Used by Amazon Q Developer, AWS Glue. Model-driven approach minimizes orchestration code.
CrewAI: Business workflows with role-based agents. Intuitive "crew" metaphor. Built on LangChain, inherits its tool ecosystem.
AutoGen: Multi-agent conversations, natural for research/experimentation. Conversational paradigm.
SmolAgents: Minimal dependencies (~1000 lines). Agents write code instead of using predefined tools.
Critical Decision: How many agents and tools per agent?
✓ Well-scoped: "Process clothing returns under $500, verify purchase date, generate labels"
❌ Too broad: "Handle all customer service inquiries"
❌ Too narrow: Separate agents for each return reason
Multi-agent evolution:
Autonomy: Each agent should complete its business outcome independently
Boundaries: Clear handoffs between agents, no overlapping responsibilities
Testability: Deterministic outcome (not behaviour) within scope boundaries
Well-Bounded Agent Example: Name: Return Processing Agent Outcome: Complete return requests autonomously Tools: Order API, Inventory check, Refund processor, Label generator Boundaries: Orders < $500, standard items only Handoffs: Escalate special cases to human agent
Route queries to appropriate models based on complexity scoring:
Implementation note: Start with rule-based routing, evolve to ML-based classification once you have data.
90% cost reduction on cached tokens
30-40% hit rate in production
Sub-10ms response for repeated queries
Implement in order: Result caching → Semantic caching → Prompt caching
Build resilience through graceful degradation:
Key insight: Define "safe defaults" for each agent action during design phase.
Governance intensity varies by environment and risk:
Staging Environment: Comprehensive governance
Production Environment: Risk-based governance
Duration: 2-4 weeks | Assessment Period
Evaluate pilot results and choose the optimal scaling strategy for your organization.
Duration: Ongoing | Scaling Period
Deploy at scale with robust monitoring, optimization, and continuous improvement.
Where to deploy full autonomy vs. human oversight:
High-Agency Zones:
Low-Agency Zones:
Production data shows agents consume significantly more tokens than traditional chat interfaces:
Chat baseline:
$0.10 per 1000 interactions
Single agent:
$0.40 per 1000 decisions (4x)
Multi-agent:
$1.50 per 1000 decisions (15x)
Source: Anthropic production deployment data
Key Principle: "When errors occur, we can't just restart from the beginning: restarts are expensive and frustrating for users." - Anthropic Engineering
Checkpoint triggers: Major decisions, API calls, state transitions, every N minutes
"We use rainbow deployments to avoid disrupting running agents, by gradually shifting traffic from old to new versions while keeping both running simultaneously." - Anthropic Engineering
Why necessary: Agent systems are stateful webs of prompts, tools, and execution logic that run continuously and cannot be interrupted.
"Beyond standard observability, we monitor agent decision patterns and interaction structures—all without monitoring the contents of individual conversations, to maintain user privacy." - Anthropic Engineering
Critical Understanding:
"Agents make dynamic decisions and are non-deterministic between runs, even with identical prompts." You cannot debug agents like traditional software. Same inputs → different paths → same outcome.
"As conversations extend, standard context windows become insufficient... agents summarize completed work phases and store essential information in external memory before proceeding to new tasks." - Anthropic Engineering
Phase Summarization: Before moving to next phase, compress completed work into key findings
External Memory: Store critical state outside context window for later retrieval
Fresh Agent Spawning: Create new agents with clean contexts for sub-tasks
Reference Passing: Share lightweight pointers instead of full content
The operational patterns in this section are derived from production experience documented in: "How we built our multi-agent research system" - Anthropic Engineering