PRACTICE 01
Define the business problem first
Write the idea, challenge, owner, baseline, expected benefit, and measurable result before discussing models.
Enterprise / Agentic development
Enterprise agentic development. Built on best practices.
Choose high-impact opportunities, establish the right data, knowledge, memory, tools, and policy foundation, and move into production with measurable results.
Agentic opportunity decision system
Ideas
Problems
Challenges
Agentic development framework
Business impactData + knowledgeMemory + toolsPolicy + model
Build now
Prepare
Stop
What the visitor needs to know
Which agentic opportunities are worth building—and is the organization ready to make them produce a definite business result?
Agentic development framework
Frame the problem. Prepare the foundation. Define the rules. Prioritize the investment.
Agentic development should not begin with a model or an experiment. It begins by defining the business problem, expected outcome, user, tools, data, knowledge, memory, authority, and policy. Only then can leaders decide where AI belongs, where deterministic software belongs, and which opportunities justify investment.
01
Frame the opportunity
Document the idea, problem statement, business challenge, process owner, affected user, current baseline, and desired result.
02
Assess the foundation
Evaluate data cleanliness, knowledge quality, memory needs, system access, tool definitions, identity, and permission boundaries.
03
Define the framework
Specify the user, agent role, tools, policies, human decisions, model choices, deterministic rules, prohibited actions, and evidence required.
04
Prioritize and prove
Score impact, readiness, result certainty, risk, cost, and time to value; select the strongest candidates for a measured proof.
Production engineering discipline
Agentic Development Best Practices
Strong agentic systems start with organizational readiness and explicit decisions. These practices connect business value to clean context, defined authority, executable AI policy, and disciplined investment.
PRACTICE 02
Prepare the context foundation
Clean the required data and establish governed knowledge and memory systems before asking an agent to reason from them.
PRACTICE 03
Define users, tools, and authority
Specify who the agent serves, which tools it may use, what actions it may take, and when a person must decide.
PRACTICE 04
Make AI policy executable
Turn model choices, approved uses, prohibited uses, privacy, security, retention, and escalation rules into system controls.
PRACTICE 05
Prioritize impact and certainty
Select opportunities with meaningful benefit, sufficient readiness, repeatable results, manageable risk, and a clear path to adoption.
PRACTICE 06
Prove value before scaling cost
Set success thresholds and cost boundaries, run a measured proof, and stop ideas that cannot outperform the current process.
Prioritization framework
Pick the opportunities worth building.
A scattered list of AI ideas becomes a ranked investment portfolio: build now, prepare the foundation, defer, or stop.
OUTCOME 01
Business impact
Estimate revenue, capacity, speed, quality, or risk improvement against an owned business outcome.
OUTCOME 02
Foundation readiness
Assess whether the required data, knowledge, memory, tools, identity, and permissions can support the use case.
OUTCOME 03
Result certainty
Favor work where AI can produce a testable, repeatable result—not merely an interesting demonstration.
OUTCOME 04
Economics and risk
Compare implementation effort, model and operating cost, consequence of error, controls, and time to value.
What becomes tangible
Artifacts the organization can use next.
OUTPUT 01
Agentic idea and problem inventory
OUTPUT 02
Data, knowledge, and memory readiness assessment
OUTPUT 03
User, tool, and policy definitions
OUTPUT 04
Model and non-model decision record
OUTPUT 05
Prioritized opportunity portfolio
OUTPUT 06
Business case and measured proof plan
Enterprise transformation