Agentic Systems
Agents that plan, act and report — under engineering control.
Autonomous and semi-autonomous systems that plan, use tools, hold memory and coordinate — with the control an enterprise requires.
Architectural layers
Problem
What problem does this solve?
- Workflows span many tools and systems; no single API call can complete them.
- Naive agent loops are unpredictable, expensive and impossible to audit.
- Organizations want automation with accountability, not black-box autonomy.
Scope
What CAS builds
- Agent runtimes with tool calling, structured memory and explicit planning loops.
- Multi-agent systems with defined roles, message contracts and supervision.
- Human-in-the-loop checkpoints, policy gates and full action audit trails.
- Agent evaluation suites: task success, tool accuracy, cost per task, safety.
Architectures
Architectures that may be used
Each links into ModLens, the CAS architecture explorer, where the structure and trade-offs are diagrammed.
Agent architecture
open in ModLens →
Memory architecture
open in ModLens →
Mixture-of-Experts routing
open in ModLens →
Context grounding
open in ModLens →
Method
How CAS approaches engineering
- Autonomy is a dial, not a switch — every deployment starts supervised and earns scope.
- Tools are APIs with schemas, permissions and rate limits; agents never hold unbounded power.
- Every plan step, tool call and result is logged into an auditable trace.
- Budget envelopes (tokens, time, cost) are enforced by the runtime, not the prompt.
Outcomes
What can result
- Workflows that complete end to end with measurable success rates.
- Auditable automation: every action traced and attributable.
- A safe path from assisted to autonomous operation.
Delivery
What the process looks like
01 · Task decomposition
Which workflows benefit from agency; where humans must stay in the loop.
02 · Tool surface
Define the action space: tools, schemas, permissions, guardrails.
03 · Agent design
Planning loop, memory model, supervision points and failure recovery.
04 · Evaluation
Task suites, adversarial cases, cost-per-task and safety testing.
05 · Rollout
Shadow mode, staged autonomy, monitoring dashboards and kill switches.
Preparation
What a client should prepare
- A workflow with clear completion criteria.
- Systems the agent may act on, with API access or a safe simulation of them.
- Agreement on which actions require human approval.
Outcomes depend on data, constraints and integration reality. CAS states assumptions explicitly and reports negative results when evidence demands them.
Discuss agentic ai with an engineer.
Bring the problem; we will bring the architecture, the evaluation plan and the honest feasibility read.