Inside An AI Agent
AI agent demos look great in videos. They often fail when you ask real questions.
The agent does the wrong thing. It forgets decisions. It calls tools that do not exist. It loops forever. These are not model failures. They are workflow failures.
An agent is a software workflow. A language model picks the next step and calls tools. The intelligence is in the orchestration, not in the model alone.
Every production agent relies on five pillars:
- Planning: Thinking before acting.
- Tool Use: Interacting with the world.
- Memory: Storing context and facts.
- Constraints: Setting boundaries and budgets.
- Verification: Proving the work is correct.
Planning Styles
Naive agents jump straight to action. This leads to hallucinations. A good agent plans first.
- Plan-then-execute: The model writes a full plan. It is easy to audit but hard to adapt if reality changes.
- ReAct (Reason + Act): The model thinks, acts, and observes in a loop. It is more adaptive but costs more in tokens and time.
Tool Use
Without tools, an agent is just a chatbot. A tool needs a name, a JSON schema, and a function.
Models pick tools based on descriptions. If your description is vague, the agent will fail. Treat descriptions like spec sheets. Define what a tool is for and what it is not for.
Always validate tool calls. If a model sends bad arguments, reject the call and feed the error back to the model. This helps the agent learn mid-loop.
Memory
Memory is not just one thing.
- Working Memory: The current conversation and tool results.
- Scratchpad: A place for the agent to write notes to itself.
- Long-term Memory: Storing facts for future sessions.
Do not rely solely on vector databases. File-based memory using markdown files is often better. It is easy to audit, edit, and grep.
Constraints and Safety
Production agents need guardrails. Use these four:
- Tool allowlists: Only allow specific, named tools.
- Iteration budgets: Cap the number of steps to prevent infinite loops.
- Token budgets: Limit costs by capping total tokens.
- Approval gates: Require human permission for high-stakes actions like sending emails or deleting data.
Verification
Never trust a model when it says a task is done. The model is confident by default.
Use real verification. If an agent writes code, run the tests. If it generates JSON, check the schema. If it performs a task, run a query to confirm the change.
The best agents use verification inside the loop. If a test fails, feed the error back to the agent. Let it try again.
Stop chasing smarter prompts. Start building better plumbing.
Optional learning community: https://t.me/GyaanSetuAi