AI SERVICES · AGENTIC SYSTEMS & TOOL USE
Agents are systems. Build them like systems.
RankSaga designs and operates multi-step agentic systems for the workflows where the cost of a wrong action is high. Tool and function calling, planning loops, human-in-the-loop checkpoints, structured output contracts, and the audit trail that makes the system safe to put in front of an operator.
An agent is a loop. The loop reads, decides, calls a tool, observes the result, and decides again. Most agent failures are not model failures. They are failures of the surrounding loop, the tool contracts, the planner, the recovery logic, and the human checkpoint that should have been there.
WHY THIS MATTERS
Agents fail in ways monolithic LLMs do not.
An agent multiplies the failure modes of an LLM by the number of steps it takes. A confident wrong tool call cascades. A planner that loses track of state takes the wrong branch. A tool with a fragile contract returns malformed data and the agent dutifully passes it forward. A human-in-the-loop checkpoint that was supposed to catch the bad action was never wired in.
RankSaga's agent work concentrates on the engineering layers that determine whether an agent ships at all. Tool contracts that are strict on input and structured on output. Planners that can be inspected, halted, and resumed. Recovery logic that handles malformed observations without spiralling. Human-in-the-loop checkpoints designed for the actual decision the operator is making, not as an afterthought. And the audit trail that lets a human reconstruct exactly why the agent did what it did, three months later.
We engage where the stakes justify the engineering. Operations consoles where a wrong action is expensive. Customer-facing flows where attribution is non-negotiable. Internal automation that touches systems of record. The Agentic AI product page covers what we have built; this page covers how we work with customers to build their own.
WHAT WE SHIP
Six concrete pieces of work.
01 / Capability
Tool and Function Contracts
Strict-input, structured-output tool definitions. Schema validation on every call, retry and backoff on transient failure, hard-fail on contract violation rather than silent corruption.
02 / Capability
Planner Architecture
ReAct, plan-and-execute, deliberate planning, or lighter routing as the workload requires. Inspection points, state checkpoints, and resumable execution where the operator needs them.
03 / Capability
Human-in-the-Loop
Checkpoints designed for the actual decision being made. Diff views, approval flows, structured rationale prompts, and the latency budget the workflow can tolerate.
04 / Capability
Memory and State
Episodic memory for the current task, persistent memory for cross-session context, and the scoping rules that prevent memory from leaking across users or trust boundaries.
05 / Capability
Recovery and Guardrails
Loop budgets, cost budgets, action limits, and the recovery logic for malformed observations. The agent halts visibly rather than spiralling silently.
06 / Capability
Audit Trail and Eval
Every step logged, every tool call attributable, every decision reconstructable. Plus the eval framework that scores plan quality, tool-call correctness, and end-to-end task completion.
HOW WE OPERATE
Constraints first, agent second.
01 / Step
Map the Workflow
We sit with the team that owns the workflow today. Decision points, the cost of a wrong action, the operator review steps that are non-negotiable, the systems of record the agent will touch.
02 / Step
Build Inside the Real Environment
Tool contracts against your real APIs and data. Planner tested on real cases from week one. HITL checkpoints designed with the people who will actually approve the actions.
03 / Step
Operate Under Tempo
Production agent inside your environment with audit trail wired in. Drift detection on plan quality and tool-call correctness, and the next round of capability as the workflow evolves.
WHAT YOU GET
An auditable agent in production.
01 / Deliverable
A working production agent
Inside your environment, integrated with your real systems. Operator-facing surface, audit trail, and HITL checkpoints designed for the decision being made.
02 / Deliverable
Strict tool contracts
Schema validation, structured outputs, retry logic. Tool failures are visible and recoverable rather than silent.
03 / Deliverable
An evaluation framework
Plan-quality scoring, tool-call correctness, end-to-end task completion. The regression suite that catches degradation before an operator does.
04 / Deliverable
Audit and reconstructability
Every step, every tool call, every decision logged. The artefacts that let your compliance and audit teams verify the system is operating within its envelope.
PROOF
Agentic AI is something we have built, not something we have read about.
RankSaga's Agentic AI product is in production. The same engineering team builds custom agentic systems for enterprise customers, and the same team operates an AI application in production for the Australian Armed Forces. The disciplines, strict contracts, audit trails, human-in-the-loop, transfer directly.
RANKSAGA · AGENTIC AI · ADF DEPLOYMENT · 2026
- ·Agentic AI product in production. See /product/agentic-ai/.
- ·Custom agentic systems shipped inside customer VPC and on-premise environments.
- ·Audit trail and HITL discipline carried over from defence work.
- ·Same engineering team that operates AI in live ADF deployment.
RELATED CAPABILITIES
Where agentic systems connect.
Adjacent
Retrieval-Augmented Generation →
When retrieval is one tool in the agent's toolbox. Grounded answers, attribution, refusal.
Adjacent
AI Evals & Observability →
Plan-quality, tool-call correctness, end-to-end task completion scoring.
Adjacent
Agentic AI Product →
Our product surface for multi-step agents under operator review.
QUESTIONS
What customers ask before we start.
When should we use an agent vs a single LLM call?+
Use a single call when the task is well-bounded, the answer can be produced in one step, and the cost of a wrong answer is recoverable. Use an agent when the task requires multi-step reasoning, real tool use against external systems, or branching logic that would otherwise live in a brittle prompt. We help size that decision honestly rather than pushing the more complex option.
What planning approach do you use?+
Whichever fits the workload. ReAct for short, exploratory tasks. Plan-and-execute when the plan should be inspectable and reviewable before execution. Deliberate or tree-of-thought planning when the cost of a wrong branch is high enough to justify the latency. Choice is driven by the workload and the operator review model.
How do you keep agents from spiralling?+
Hard limits on loop depth, action count, cost per task, and clock time. Recovery logic on malformed observations. Halt-and-escalate when the agent leaves its envelope rather than retry-until-success. Agent runaway is an engineering problem and is solved with engineering controls.
Where does human-in-the-loop fit?+
Wherever the cost of a wrong action exceeds the cost of operator latency. We design HITL checkpoints around the actual decision the operator is making, with the right context, the right diff view, and the right structured rationale prompts. Not as a generic 'approve-or-reject' bolt-on.
Can the agent's tool calls be audited?+
Yes, and for regulated workloads they must be. Every tool call is logged with inputs, outputs, the planner state at the time of the call, and the rationale the model provided. The audit trail is queryable by your compliance and incident teams.
Can you work inside our VPC, on-prem, or air-gapped environment?+
Yes. The same engineering team that ships agentic systems for commercial customers operates an AI application in air-gapped production for the Australian Armed Forces. Deployment surface is selected against the customer's residency and connectivity constraints.
ENGAGE
If you have a workflow where multi-step matters and the audit trail is non-negotiable, we want to look at it.
Most agent engagements we see start with a workflow the team has been trying to automate for months. The work is to design the loop that holds up under operator review and production traffic.