Developer Assistant

Agent Runtime v3: native Model Context Protocol (MCP) tools, full function-call traces, sandboxed Code Mode execution, and error-class-aware retries.

<10%

Agent Failure Rate

Minutes

Root Cause Analysis

99.9%

Tool Call Success

3×

Dev Efficiency

Challenge

Pain points in AI Agent development

Unpredictable Calls

Agent tool behavior is hard to predict, causing production issues

Hard to Debug

Agent failures lack context, making troubleshooting time-consuming

Unreliable Retries

Simple retry strategies cause idempotency issues and waste resources

Black Box Execution

Complex Agent flows are opaque, hard to understand and optimize

System Architecture

Solution

SkyAIApp Agent Runtime Platform

Native MCP tool protocol

Anthropic's Model Context Protocol is the 2026 industry default — Tools / Resources / Prompts work unchanged across OpenAI, Anthropic, Google, Microsoft.

Full traces incl. function calls

Every LLM call → function pick → MCP tool call → result → next call has a span. Replay any failure with one click.

Error-class-aware retries

Separate transient / rate-limit / contract / hallucination paths — exponential backoff, cross-provider fallback, idempotency checks, or human handoff.

Versioned tool schemas

JSON-schema'd tool descriptors are versioned with gradual rollout and back-compat checks — agents can't drift onto a wrong call signature.

Code Mode sandboxes

For compute and data-shaping tasks, agents can write code that runs inside isolated WASM/V8. 5-10× faster than chains of individual tool calls.

Agent evaluation harness

Bundled eval sets (HotpotBench / SWE-bench Lite / your own cases) auto-run in CI on every prompt change.

Modeled Results

<10%

Agent Failure Rate

Sandboxing and smart retries significantly reduce failures

Minutes

Root Cause Analysis

Full tracing reduces debugging from hours to minutes

99.9%

Tool Call Success

Retries and fallbacks ensure high availability

3×

Dev Efficiency

Visual debugging and rapid iteration boost productivity

Integration ecosystem

MCP Servers

GitHub / Linear / Slack native MCP

OpenAI Agents SDK

Python / TS bidirectional

Anthropic Computer Use

Screen / browser agents

LangGraph · CrewAI

MCP-first by default

Datadog · Langfuse

Trace visualization

GitHub Actions

CI eval runs

Composite profile — CodeForge Labs-style dev-tools platform

This composite profile models a dev-tools platform running 12K+ agent tasks per day (IDE autocomplete + PR review + bug fix). The baseline keeps about 4000 lines of in-house retry / sandbox / trace plumbing and sees 3 silent failures per week needing on-call; the SkyAIApp Agent Runtime replay model lowers P0 failure rate from 1.4% to 0.08%.

Key call: completion uses GPT-5.5 Instant + 50ms cache (67% hit rate); PR review escalates to Sonnet 4.6 with Opus 4.7 advisor; code execution always goes through SkyAIApp sandbox (isolation + resource caps + auto cleanup).

Composite-profile quote: “SRE used to fix stuck agents twice a week. The target state is runtime auto-abort + retry, with traces showing exactly which tool failed.”

Tech stack

CompletionGPT-5.5 Instant
PR reviewSonnet 4.6 + Opus 4.7 advisor
Code execSkyAI sandbox (Wasm)
Code ModeCodestral 3 + Mistral Medium 3.5
ToolsGitHub MCP + Jira + Notion
TracingOTLP → Honeycomb

Implementation timeline

Week 1

Replace retry / sandbox

Swap in-house retry / sandbox for SkyAIApp Agent runtime; keep external API unchanged.

Week 2

Tools → MCP

Move GitHub / Jira calls to MCP tools; agent picks up permission boundaries automatically.

Week 3

Trace wire-up

OTLP exporter to Honeycomb; P0 alert on runtime.task_timeout pages on-call.

Week 4

Code Mode + 100%

Code-gen on the Codestral 3 + Mistral Medium 3.5 ladder; cut 100% traffic.

Agent runtime configuration

import { SkyAI, defineTool } from "@skyaiapp/sdk";
import { z } from "zod";

const sky = new SkyAI({ apiKey: process.env.SKYAIAPP_API_KEY! });

// Custom MCP tool — typed and sandboxed.
const runTests = defineTool({
  name: "run_tests",
  description: "Run the test suite in the project sandbox.",
  parameters: z.object({ paths: z.array(z.string()).optional() }),
  returns:    z.object({ passed: z.number(), failed: z.number(), duration_ms: z.number() }),
  handler:    async ({ paths }) => sandbox.runTests(paths),
});

export async function reviewPullRequest(prId: string) {
  const agent = sky.createAgent({
    tools: [
      "github.fetch_pr",
      "github.fetch_diff",
      "github.post_comment",
      runTests,
      "code_exec",                            // built-in Wasm sandbox
    ],
    maxSteps: 12,
    perStepTimeoutMs: 60_000,
    totalBudgetUsd: 0.30,                     // hard cap per PR
    modelStrategy: { goal: "quality", strategy: "quality-first" },
    advisor: { model: "claude-opus-4.7", whenStuckSteps: 3 },   // pull in Opus when stuck
    fallback: { models: ["claude-sonnet-4.6", "gpt-5.5-pro"], maxRetries: 1 },

    // Per-step observability — every event also lands in OTLP -> Honeycomb.
    onStep: (s) => log.info("agent.step", {
      pr: prId, num: s.number, action: s.action, tool: s.tool, ms: s.durationMs,
    }),

    metadata: { tenant: "codeforge", workflow: "pr-review", pr_id: prId },
  });

  return agent.run({
    task: `Review PR ${prId}: check correctness, run tests, post a single concise review comment.`,
  });
}

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