Agentic Systems

Agentic Workflow Design: Decomposition, Planning, and Handoffs

An effective agent is not a single prompt with tools attached. It is a system that breaks complex tasks into steps, plans execution order, handles failures gracefully, and knows when to hand off to another agent or a human. We design agentic workflows with explicit task decomposition, retry logic, and handoff protocols that make multi-step execution reliable and observable.

Each workflow is modelled as a directed graph with defined entry points, decision nodes, and exit conditions. This structure makes the agent's behaviour predictable, testable, and explainable to stakeholders who need to understand what the system does before approving it for production.

Fry Express builds workflows incrementally. We start with a narrow, well-defined task, validate it end to end, and expand scope only after the foundation is proven.

Tool Integration Across APIs, Databases, and Internal Systems

Agents derive their value from the tools they can use. We integrate agents with APIs, ticketing systems, document stores, databases, CI/CD pipelines, and internal platforms so they can execute real work rather than just generate text.

Each tool integration includes input validation, error handling, and output normalisation. The agent receives structured, predictable responses regardless of the underlying system's quirks. This isolation layer means that changing a backend system does not require rewriting the agent's logic.

Tool access is governed by the same permission model that applies to human users. An agent cannot query a database or trigger a deployment unless it has been explicitly granted that capability.

Human-in-the-Loop Checkpoints for High-Risk Actions

Not every action should be automated without oversight. We build human-in-the-loop checkpoints at defined points in the workflow where the cost of an error is high: financial transactions, data modifications, external communications, or infrastructure changes that affect production.

Checkpoints present the agent's proposed action, the reasoning behind it, and the expected outcome. The human approver can accept, reject, or modify the action before execution continues. Rejected actions are logged with the reason, creating a feedback signal that improves future agent behaviour.

The checkpoint mechanism is designed to be lightweight. Approvals happen in the tools your team already uses, whether that is Slack, Teams, or a dedicated review interface. The goal is informed oversight, not bureaucratic friction.

Security, Audit Controls, and Data Access Governance

An agent that can call tools and access data must be governed with the same rigour as any privileged service account. We implement permission boundaries that define exactly which tools, data sources, and actions each agent can access. Every action is logged with full context: what was requested, what was executed, and what data was read or written.

Data access governance ensures that agents operating in environments with sensitive information respect classification rules, access controls, and retention policies. An agent processing customer data in a production environment operates under different constraints than one running in a development sandbox.

Fry Express treats agent security as a first-class concern, not a layer added after the workflow is built. Permissions are defined during design and enforced at runtime.

Cost Telemetry Per Workflow With Budget Caps and Graceful Degradation

Agentic workflows can consume significant resources, especially when they involve multiple LLM calls, tool invocations, and retry loops. We instrument per-workflow cost telemetry that tracks token usage, tool call costs, and compute time for every execution.

Budget caps prevent runaway workflows from exceeding defined spend limits. When a cap is approached, the agent can switch to cheaper models, reduce the number of reasoning steps, or terminate gracefully with a partial result and a clear explanation of why it stopped.

This ensures that cost is a design constraint, not a post-mortem finding. Teams can set budgets per workflow type and adjust them as they learn what reasonable execution costs look like.

Reliability Engineering With Evaluation and Regression Testing

Agents operating in production must meet the same reliability standards as any other service. We apply idempotency guarantees, fallback strategies, and rate limiting to ensure that retries do not cause duplicate side effects, provider outages do not halt workflows, and burst traffic does not overwhelm downstream systems.

Every agent workflow is covered by evaluation and regression tests that validate behaviour against expected outcomes. When a tool integration changes, a model is updated, or a workflow is modified, the test suite catches regressions before they reach production.

Fry Express builds reliability into the agent from the start. The result is a system that operations teams can run with confidence, not a prototype that works in demos but fails under real conditions.

These deliverables produce agentic systems that are production-grade by design: governed, observable, cost-controlled, and tested. The agents do real work within defined boundaries, and the teams responsible for them have the tools and confidence to operate them at scale.