Agent-shaped work
Interpretation with bounded action
- The goal is clear but the path varies
- Tools and data can be scoped
- Results can be evaluated
- Uncertainty can reach a human
Observable agent operations
AI agents you can inspect before you trust.
AI agents are systems that interpret goals, plan, call approved tools, and complete multi-step work. FISTA engineers production-grade agents with explicit permissions, human checkpoints, evaluation contracts, observability, and evidence trails so consequential workflows can gain useful autonomy without turning behavior, access, or failure into a black box.
- 01GOALResolve a supplier exception with attributable evidence
- 02PLANGather policy → inspect record → draft next action
- 03TOOL / READApproved: policy.search + record.fetch
- 04CHECKPOINTHuman approval required before external write
- 05EVIDENCESources, tool results, decision, and reviewer retained
Agent suitability
Should this workflow be an agent?
Use an agent when the work requires interpretation across multiple steps, has tools the system can safely access, provides feedback or evidence, and can escalate ambiguity. Prefer deterministic automation when rules are stable, inputs are structured, and the correct path is known. FISTA makes this decision before selecting an agent architecture.
Keep deterministic
Known rules with exact execution
- The sequence is fixed
- Inputs and outputs are structured
- Variance creates no useful value
- A normal service or workflow engine is clearer
Bring the real workflow, systems, and failure conditions. FISTA will help determine whether it needs an agent at all.
Map an agent workflowControl plane anatomy
What makes agent autonomy observable?
The control plane separates intent from execution. Goals and policies shape planning; identity determines available tools; checkpoints stop consequential actions; evaluation tests quality; traces retain evidence. This architecture lets operators understand what happened, intervene where required, and improve the system without granting broad, opaque autonomy.
Policy boundary
The agent sees only the tools, resources, and actions authorized for this workflow and identity.
- Read and write separated
- Sensitive actions gated
- Escalation is explicit
- Evidence is retained
Goal + context
The task, completion condition, and relevant policy.
Planner
Selects a next step within the allowed boundary.
Tool gateway
Validates identity, arguments, permission, and result.
Human checkpoint
Pauses the run before high-consequence action.
Evaluator
Assesses correctness, grounding, and task completion.
Trace store
Retains the evidence needed to inspect behavior.
Permissions + guardrails
Where should an agent stop and ask?
Checkpoints belong where the cost of a wrong action changes: external communication, money movement, record mutation, privileged access, legal commitment, or irreversible operations. FISTA defines those boundaries in the workflow specification, enforces them in tool permissions, and tests that the agent pauses rather than improvises around them.
Action classDefault authorityRequired evidence
Search / retrieveRead within scopeSource IDs + query
Draft / recommendNo external side effectInputs + rationale
Write / sendHuman checkpointProposed change + approver
Privileged / irreversibleExplicit policy gateIdentity + approval + result
Evaluation contract
How is an agent tested before production?
Evaluation begins with a contract for acceptable behavior: the task set, expected evidence, prohibited actions, escalation rules, and quality thresholds agreed for the workflow. FISTA tests representative, edge, and adversarial cases, then carries the same signals into production traces so launch does not end the learning loop.
Contract / example fields
- Completion
- What observable result counts as done?
- Grounding
- Which claims require attributable evidence?
- Safety
- Which action must never bypass policy?
- Escalation
- When should uncertainty reach an operator?
- Trace
- What must be retained for review?
Workflow map / required inputs
Put the workflow on the table before the model.
A mapping session defines the autonomy boundary and the evidence needed to decide whether an agent should be built.
- 01Goal and completion condition
- 02Systems, identities, and allowed tools
- 03Consequential actions and approval points
- 04Evaluation cases and retained evidence
Operator questions / traceable answers
01What is an AI agent?+
An AI agent is software that uses a model to interpret a goal, plan work, call approved tools, inspect results, and continue until a defined completion condition or human checkpoint. Unlike a chatbot, an agent acts across a workflow and must therefore be permissioned, evaluated, and observable.
02What can FISTA’s AI agents automate?+
Good candidates include multi-step research, data processing, internal operations, ticket workflows, and tool orchestration. FISTA first tests whether the work has clear boundaries, useful feedback, accessible systems, and a sensible escalation path; some workflows are better served by deterministic automation.
03How do you keep autonomous agents safe and reliable?+
FISTA scopes tool permissions, validates inputs and outputs, introduces human approval for consequential actions, records execution evidence, tests representative and adversarial cases, and defines fallbacks. The controls are matched to the workflow’s actual failure cost rather than presented as a universal guarantee.
04Can agents integrate with our existing tools?+
Yes. Agents can use permissioned interfaces to APIs, databases, knowledge sources, and business platforms. Discovery identifies which operations are read-only, which may write, which require approval, and what evidence must be retained so every important action can be traced.