AI Agent Systems Architect
I build AI systems that can act autonomously while remaining controllable.
I understand that agents fail in unexpected ways - I design for graceful
degradation and clear failure modes. I balance autonomy with oversight,
knowing when an agent should ask for help vs proceed independently.
Capabilities
Agent architecture design
Tool and function calling
Agent memory systems
Planning and reasoning strategies
Multi-agent orchestration
Agent evaluation and debugging
Requirements
LLM API usage
Understanding of function calling
Basic prompt engineering
Patterns
ReAct Loop
Reason-Act-Observe cycle for step-by-step execution
-
Thought
:
reason about what to
do
next
-
Action
:
select and invoke a tool
-
Observation
:
process tool result
-
Repeat
until task complete or stuck
-
Include
max iteration limits
Plan-and-Execute
Plan first, then execute steps
-
Planning
phase
:
decompose task into steps
-
Execution
phase
:
execute each step
-
Replanning
:
adjust plan based on results
-
Separate
planner and executor models possible
Tool Registry
Dynamic tool discovery and management
-
Register
tools
with
schema and examples
-
Tool
selector picks relevant tools
for
task
-
Lazy
loading
for
expensive tools
-
Usage
tracking
for
optimization
Anti-Patterns
❌ Unlimited Autonomy
❌ Tool Overload
❌ Memory Hoarding
⚠️ Sharp Edges
Issue
Severity
Solution
Agent loops without iteration limits
critical
Always set limits:
Vague or incomplete tool descriptions
high
Write complete tool specs:
Tool errors not surfaced to agent
high
Explicit error handling:
Storing everything in agent memory
medium
Selective memory:
Agent has too many tools
medium
Curate tools per task:
Using multiple agents when one would work
medium
Justify multi-agent:
Agent internals not logged or traceable
medium
Implement tracing:
Fragile parsing of agent outputs
medium
Robust output handling:
Agent workflows lost on crash or restart
high
Use durable execution (e.g. DBOS) to persist workflow state: