Building Reliable Agents with Traces and Retries

Agents call tools, write data, and chain tasks. That’s powerful — and fragile.

In production, failures are rarely obvious:

• A tool times out after partial work.
• A function call succeeds but returns malformed data.
• One model output triggers a cascade of retries.

SkyAIApp’s Agent Runtime provides SRE‑grade controls so you can treat an agent like any other production service.

Why “works in a demo” is not enough

Agents amplify small issues:

• one flaky tool can poison the whole workflow
• one bad parse can send the agent into a loop
• one retry without idempotency can duplicate side effects

Unlike chat completions, agents usually touch real systems: tickets, invoices, shipping labels, CRM updates. Reliability is not optional.

Case study: when retries cause real damage

LogiFleet (anonymized logistics platform) shipped an agent that:

1. Reads a shipment request.
2. Calls a pricing tool.
3. Books the shipment in a carrier API.
4. Sends a confirmation email.

The demo was flawless. The first production week wasn’t.

Incident

• Carrier API p95 latency spiked from 700ms to 2.4s during peak.
• The agent retried the “book shipment” tool on timeout.
• The carrier API sometimes succeeded after the timeout, so retries created duplicate bookings.

Impact

• ~0.7% of bookings were duplicated.
• Support had to manually cancel and refund fees.
• Engineering temporarily disabled the agent.

Fix with SkyAIApp Agent Runtime

• Added idempotency keys to all tools with side effects.
• Set one fast retry with exponential backoff and a circuit breaker.
• Enabled schema validation on tool outputs.
• Added trace alerts when retry budget exceeded 2%.

After rollout:

• Duplicate bookings dropped to effectively zero.
• Success rate rose to 99.5%.
• p95 latency stabilized because fallbacks avoided long waits.

The four controls every agent needs

1) Idempotent execution

If a tool can modify state, retries must be safe. SkyAIApp automatically:

• attaches a stable runId and stepId to tool calls
• supports deduping at the runtime layer
• exposes helper utilities for idempotency in your tool adapters

2) Bounded retries + circuit breakers

Retries are useful, but unbounded retries turn into loops and cost spikes. Best practice:

• 0–1 immediate retries for transient errors
• exponential backoff for network‑bound tools
• hard caps per step and per run
• circuit breaker if a tool family degrades

SkyAIApp enforces retry budgets and surfaces “retry heatmaps” in traces.

3) End‑to‑end traces

Agent traces should show:

• model spans (tokens, latency, cost)
• tool spans (inputs, outputs, timeouts)
• policy decisions (why a pool was chosen)
• retry events (what failed, what recovered)

With SkyAIApp you can replay any run, inspect the exact prompt and tool payloads, and compare to a new prompt version before deploying.

4) Structured tool IO

Many production failures are parsing failures. SkyAIApp lets you:

• define JSON schemas for tool outputs
• validate tool returns automatically
• route malformed returns to a safer fallback

This is especially important for multi‑step reasoning where one wrong field can derail planning.

Measuring reliability like an SRE team

Once controls are in place, define SLOs:

• Agent run success rate (target ≥ 99%)
• Tool error budget per tool family
• Retry overhead as % of total cost
• p95 run duration

SkyAIApp dashboards break these down by policy version, model pool, and intent class. That makes it obvious whether a new prompt or a new model improves reliability.

Shipping agents safely

A practical rollout sequence:

1. Start with read‑only tools.
2. Add side‑effect tools only after idempotency is implemented.
3. Enable traces and set alert thresholds.
4. Introduce fallbacks for high‑risk steps.
5. A/B new prompts and policies before full rollout.

Agents are the highest‑leverage AI pattern, but only when the runtime is engineered for failure. With traces, bounded retries, and idempotency, you can ship them with confidence.