Case Study
Value Discovery & Portfolio
Prioritize GenAI investments with explicit value hypotheses, scale criteria, and stop rules so spend concentrates on initiatives that can reach production safely.
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Agentic AICase Study
Agentic AI Architecture and Controls
Design and operate agentic AI with governed tool access, identity and permission scoped execution, managed memory, and runtime budgets. Use approval boundaries and traceability so autonomy stays safe in real operational workflows.
Executive Outcome
01
Governed tool use through explicit contracts, identity scoped permissions, and memory boundaries, reducing invalid calls, unsafe inputs, and uncontrolled side effects.
02
Controlled execution for high-stakes actions through approval paths, reversible-action policies, and runtime budgets, limiting blast radius and preventing irreversible errors.
03
Decision records, runtime traces, and memory provenance that support post-incident analysis and continuous control refinement over time.
Engagement focus
Architecture foundations and runtime control points for agentic systems, including identity, memory, runtime budgets, and tool governance.
Context
Agentic systems shift the primary risk from 'bad content' to 'bad actions.' The objective is to enable autonomous workflows while ensuring tool access is permissioned, side effects are controlled, and both reasoning and execution are traceable for audit sampling and incident response. In practice, this requires explicit agent foundations for identity, memory, and runtime, so autonomy is anchored to verifiable permissions, state, and execution limits.
The Challenge
- 01Agents could generate invalid parameters, misuse tools, or operate outside intended constraints, increasing operational and security risk.
- 02Insufficient traceability of why actions were selected, which identity and permissions were used, and what checks were applied, limiting debuggability and assurance.
- 03Looping and runaway execution risked compounding errors, escalating side effects, and consuming excessive resources.
- 04Unclear delegation and confirmation boundaries increased the chance of sensitive or high-impact actions being executed without appropriate oversight.
- 05Uncontrolled memory growth and poor memory hygiene introduced privacy risk, stale context, and non-repeatable behavior across sessions.
- 06Runtime resource usage was not bounded, causing runaway cost, latency spikes, and unpredictable failure modes under load.
Approach
- →Defined a tool registry with strict tool contracts covering schema, scope, idempotency expectations, permission requirements, and side-effect classification.
- →Defined an identity and permission model for agents, including scoped credentials, session identities, and least privilege tool authorization.
- →Established an action policy that distinguishes reversible versus irreversible actions and routes high-impact actions through explicit approval steps.
- →Established a memory model with explicit memory classes, retention rules, and provenance, separating ephemeral context from durable records and preventing sensitive data persistence.
- →Enforced governed autonomy through delegation boundaries, runtime budgets, and risk-based execution rules (what can run autonomously, under which conditions).
- →Implemented runtime controls including budget propagation, timeouts, rate limits, and step ceilings, with deterministic failure handling and safe fallbacks.
- →Implemented decision record and trace stitching formats to capture intent, identity, permissions, memory reads and writes, enforcement decisions, evidence, and outcomes around tool use.
Key Considerations
- Stronger constraints can reduce speed and flexibility in exchange for safety, predictability, and operability.
- Tool schema and contract maintenance require ownership and change discipline as tools evolve.
- Approval paths introduce latency and must be designed to minimize operational bottlenecks and escalation overload.
Alternatives Considered
- ✕Unbounded agents: rejected due to unacceptable operational, security, and misuse risk in production environments.
- ✕Hardcoded workflows only: rejected because they fail to handle unstructured inputs and evolving edge cases while still requiring governance and traceability.
Representative Artifacts
01Tool registry specification and tool contract schema
02Identity and permission model for agents (session identity, scopes, credential handling)
03Memory model and hygiene policy (memory classes, retention, provenance, sensitive data rules)
04Action policy model (auto-approve versus human review by risk tier)
05Runtime budget and execution policy (timeouts, rate limits, step ceilings, cost caps)
06Decision record format (intent, identity, checks, enforcement decision, evidence, outcome)
07Trace stitching model (plan to tool call to outcome, including memory and permission traces)
08Connector governance checklist (credentials, scopes, rotation, approvals)
Acceptance Criteria
Verified that agents cannot call tools outside their permitted scope and contract constraints.
Verified that irreversible actions consistently trigger the defined approval path before execution.
Verified that every tool call is preceded by a recorded decision record and enforcement checks.
Verified that recovery from tool errors is demonstrated without entering runaway loops or exceeding runtime budgets.
Verified that agent identity is scoped per session and tool permissions enforce least privilege with auditable authorization decisions.
Verified that memory writes follow explicit hygiene and retention rules, with provenance for retrieved memories and no persistence of restricted data classes.
Verified that memory retrieval follows defined provenance and eligibility rules, and that retrieved memories are traceable to approved sources.
Verified that runtime budgets, timeouts, and step ceilings are enforced consistently and trigger deterministic fallback and escalation paths.
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