FDE-Agent is fdeai.agency's most-requested engagement type. It covers the design, implementation, and production deployment of AI agent systems — multi-step LLM-orchestrated workflows that use tools, maintain state, and operate with varying degrees of autonomy.
The "FDE" in FDE-Agent refers to the delivery model: an embedded engineer works inside your organization full-time during the engagement, owns the agent system end-to-end, and exits with the system in production and your team trained to own it.
What Counts as an "AI Agent System"
An AI agent system is any LLM-based system that:
- Takes a goal or instruction as input
- Produces a series of intermediate reasoning steps or tool calls
- Executes actions in external systems (APIs, databases, code execution, file systems)
- Produces a final output based on the combined results
This covers a wide range: a simple tool-use pipeline with 2–3 API calls, a complex multi-agent system with specialized sub-agents for different tasks, or an autonomous system that executes long-horizon tasks with minimal human oversight.
The common thread is LLM-orchestrated decision-making combined with action in the real world. This is what makes agents fundamentally different from simple RAG Q&A systems — and fundamentally harder to build reliably.
What FDE-Agent Engagements Cover
Multi-Agent Orchestration
Complex agent tasks require multiple specialized agents working in sequence or parallel. An orchestrator agent decomposes the task, assigns sub-tasks to specialist agents, aggregates results, and handles failures in the sub-task chain.
FDE-Agent engagements implement orchestration architectures using LangGraph (for stateful workflows), custom orchestration for specialized use cases, or CrewAI (for role-based agent coordination). Each has different tradeoffs in flexibility, debugging support, and operational complexity.
Tool Integration
Agents are powerful only when connected to the right tools. FDE-Agent builds production-grade tool integrations: API calls, database queries, code execution, web search, file operations, email and calendar access — each with retry logic, timeout handling, output schema validation, and audit logging.
Tool reliability is the single biggest determinant of agent system reliability. An agent with 10 tools, each reliable 97% of the time, produces correct end-to-end results only 74% of the time (0.97^10). FDE-Agent invests heavily in per-tool reliability to achieve acceptable end-to-end reliability.
Memory and State Systems
For agents that handle multi-turn conversations or long-running tasks, FDE-Agent implements memory architectures appropriate to the use case:
- Short-term context management: Conversation history summarization, context pruning, and selective attention to prevent context overflow
- Long-term semantic memory: Vector search over past interactions and relevant knowledge for persistent agents
- Structured episodic memory: Typed records of past task executions for agents that learn from experience
- Shared state: Cross-agent shared state for multi-agent coordination
Streaming and Async Architectures
Production agents often require streaming outputs (for user-facing applications where users watch the agent "think") and async execution (for background tasks that run without a waiting user). FDE-Agent implements WebSocket-based streaming, job queue architectures (Celery, BullMQ, or cloud-native queues), and webhook-based result delivery.
Evaluation Harnesses
Every FDE-Agent system ships with an automated evaluation suite measuring:
- Task completion rate (does the agent complete the assigned task?)
- Tool call reliability (how often does each tool call succeed?)
- Safety constraint adherence (does the agent stay within its defined scope?)
- Latency distribution (how long does task completion take at P50/P90/P99?)
- Cost per task (tokens consumed × model pricing)
The eval harness runs on every code change and on a scheduled cadence in production, alerting when performance drops.
Observability
Full tracing of agent execution: each reasoning step, tool call and its inputs/outputs, intermediate state, and final response. Integration with LangSmith (for LangGraph systems), Helicone (for OpenAI/Anthropic API calls), Weights & Biases (for ML-heavy systems), or custom tracing depending on your stack.
Common FDE-Agent Use Cases
Customer support automation: Agent handles tier-1 support queries by searching documentation, querying account systems, drafting responses, and escalating edge cases to human agents. Human-in-the-loop for escalation; fully autonomous for standard queries.
Internal knowledge assistant: Agent deployed over your company's internal documentation, code, and data. Engineers and employees ask questions in natural language; the agent retrieves relevant information, synthesizes across sources, and provides answers with citations.
Document processing pipeline: Agent ingests contracts, reports, or research PDFs, extracts structured information per a defined schema, validates extraction quality, and routes outputs to downstream systems (CRMs, databases, notification systems).
Code generation and review: Agent generates code based on feature specifications, runs tests, interprets failures, iterates until tests pass, and creates a PR. Integrated with your existing CI/CD pipeline.
Data analysis agent: Agent accepts natural language data questions, writes and executes SQL queries against your data warehouse, interprets results, identifies notable patterns, and produces a structured report with supporting data.
FDE-Agent Engagement Timeline
| Weeks | Activity | |---|---| | 1–2 | Scoping, architecture design, tool inventory, eval framework spec | | 2–5 | Core agent framework, orchestration logic, baseline eval harness | | 5–11 | Tool integrations, memory system, streaming infrastructure | | 11–14 | Production hardening, observability, load testing, security review | | 14–16 | Documentation, knowledge transfer, handoff |
Timeline for a typical 16-week FDE-Agent engagement. Simpler scopes (single-agent, few tools) can complete in 8–10 weeks.
FDE-Agent Pricing
FDE-Agent engagements are fixed-scope. Total cost is agreed before work begins.
| Scope | Duration | Cost Range | |---|---|---| | Simple tool-use agent (2–4 tools) | 6–8 weeks | $80K–$120K | | Single-agent production system | 8–12 weeks | $120K–$180K | | Multi-agent orchestration system | 12–16 weeks | $180K–$280K | | Complex enterprise integration | 16–24 weeks | $280K–$500K |
Frequently Asked Questions
What orchestration framework does FDE-Agent use? We use the best tool for the job. LangGraph for stateful multi-agent workflows that require complex state machines and error recovery. CrewAI for role-based agent coordination. Custom frameworks for specialized use cases where standard tools don't fit. We don't have framework loyalty — we have production reliability requirements.
Can FDE-Agent build agents that operate autonomously without human oversight? Yes, for use cases where autonomous operation is appropriate and safe. For high-stakes decisions (financial transactions, customer-facing communications, data deletion), we recommend and implement human-in-the-loop checkpoints as part of the agent architecture. Autonomous operation and safety controls are not mutually exclusive.
What LLMs do FDE-Agent systems use? Platform-agnostic. We use Claude (excellent for long-context and tool use), GPT-4o (best general-purpose tool-calling), Gemini (strong for multimodal and large-context tasks), or open-weight models (for on-prem or cost-sensitive high-volume use cases). Model selection is based on your requirements for capability, cost, latency, and data sovereignty — not a standing preference.
How do we handle agent costs in production? We model inference cost as part of the engagement and implement cost optimization before launch: caching for deterministic tool calls, smaller models for sub-tasks (routing simple reasoning to a smaller/cheaper model), batching for non-real-time workloads, and prompt optimization to reduce token consumption.
What happens when an agent gets stuck or produces a wrong output? Production FDE-Agent systems have: timeout handling (agent steps that take too long are interrupted), confidence thresholds (agent can flag uncertainty rather than proceeding with a low-confidence action), human escalation paths (for use cases where human review is appropriate), and error classification (distinguish recoverable errors from agent logic failures). We define the failure handling spec during scoping.