Build an Agent
Deploy a Streamkap streaming agent step by step — pick an input topic, configure the model, tools, and output schema, preview a run, and deploy
Agents are created in a five-step wizard. This guide walks through each step, previewing a run before you deploy. Open Agentic → Agents and click Deploy an agent.
Prerequisites
- An LLM connection with the Chat capability — see Connections. The wizard prompts you to add one if none exist. Where available, you can pick the Streamkap Default LLM to try an agent without bringing your own key.
- An input Kafka topic the agent will read from. Any topic works — from a source, a transform, another agent, or your own producer.
- For the Streamkap MCP tool, an agentic-enabled Project Key.
Deploying and editing agents requires the write:agents permission.
Step 1 — Template
Start from a preset — Classify, Enrich, Route, or Redact PII — or choose Custom Agent to start from scratch. A preset fills in the system prompt and a matching output schema, so you only name the agent, pick an input topic, and edit from there. Presets are single-pass with no tools; for a multi-step (ReAct) or retrieval (RAG) agent, choose Custom Agent and add your own tools or knowledge bases. In edit mode this step is skipped.
| Preset | What it does | Output fields |
|---|---|---|
| Classify | Assigns each record to a single category with a confidence score. | category, confidence |
| Enrich | Adds derived fields (sentiment, priority, summary) while keeping the original record. | sentiment, priority, summary |
| Route | Chooses a destination for each record, with a short reason. | destination, reason |
| Redact PII | Masks personally identifiable information and flags whether any was found. | redacted_text, contained_pii |
Step 2 — Input
Set what the agent reads and how records reach the model.
- Agent name (required) and an optional description.
- Input topic (required) — select one topic. An agent reads from exactly one topic.
- Filter & fields (optional) — restrict which records the agent processes and which fields reach the model:
- Build a filter with the Visual Builder (field / operator / value rules) or write Raw SQL. Use Preview matching records to test the filter against recent topic data.
- Under Fields sent to the LLM, choose which fields to include. Fewer fields means lower token cost.
- Input format — auto-detected, or set the serialization (JSON or Avro) explicitly.
- Knowledge Bases (optional) — attach up to 10 running knowledge bases; the agent gets a retrieval tool for each. See Knowledge Bases.
Step 3 — Agent
Configure the model and behavior. Each row opens a side drawer.
Model
Pick a saved LLM connection — the provider, model, and key all come from it. Optionally override per-agent tuning: temperature, max tokens, timeout, and (where the model supports it) reasoning effort and extended thinking.
System Prompt
Write your custom instructions — the guidance that shapes the agent’s behavior (up to 8,000 characters). Streamkap assembles the full system prompt from a base template, your output schema, your tools, and these instructions, so the 8,000-character limit applies to your instructions, not the assembled prompt. Use Generate or Refine with AI to draft or improve them, and Show base prompt to see the full assembled prompt.
Tools
Click Add tool and choose a type:
| Tool | What it does |
|---|---|
| HTTP | Calls an external API. Optionally references an HTTP connection for the base URL and headers. Set method, URL, headers, body template, and timeout. |
| Script | Runs a JavaScript or Python transformation you write inline. |
| External MCP | Calls tools from an external MCP server. Discover tools and choose which to expose. |
| Streamkap MCP | Lets the agent operate your Streamkap platform via an agentic-enabled Project Key. |
For HTTP and Script tools, give each a name, a description (shown to the model so it knows when to call the tool), and any parameters. For External MCP, discover the server’s tools and choose which to expose. For Streamkap MCP, select a Project Key that has Use with agents enabled, and optionally restrict which platform tools the agent can call — see Streamkap MCP.
Memory
Optionally carry context across records, keyed by a record field:
- Short-term — time-boxed context with a TTL (1 hour, 6 hours, 24 hours, or 7 days).
- Long-term — persisted to a vector store connection.
Processing
- Max reasoning steps — the tool-use loop cap for ReAct agents (1–25).
- Parallelism — how many records the agent processes concurrently.
- Checkpoint interval (advanced) — how often the job checkpoints.
Step 4 — Output
Define what the agent writes.
- Output topic — where results are written (auto-derived from the agent name; editable). Each output topic can be written by only one agent.
- Output serialization — JSON or Avro.
- Output schema (optional) — declare fields and scalar types (String, Double, Integer (32-bit), Long (64-bit), Boolean; nested objects and arrays aren’t supported). The model’s response is validated against the schema; records that don’t match are routed to the dead-letter topic. With no schema, the raw LLM response is written through.
Step 5 — Review & Deploy
Review the summary cards for each section. Use the pencil on any card to jump back to that step. Then deploy — or save a draft to finish later.
- Deploy an agent — deploys the job immediately.
- Save as Draft — saves the configuration without deploying. The agent appears with a Draft status; deploy it later from the Agents list.
Preview a Run
Before deploying, use Preview to run a single record end-to-end without deploying. Paste a record (JSON) — or Pull from topic to grab a recent one — and click Run preview. Preview runs on Anthropic, OpenAI, and Ollama connections; other providers (Azure, Azure OpenAI, AWS Bedrock, OpenAI Responses) run only once the agent is deployed.
Preview shows the outcome (completed, stopped at the step cap, timed out, or errored), the iterations, tokens in and out, latency, a step-by-step timeline, and whether the output matches your schema.
After Deploying
The agent starts processing its input topic. Track it from the Agents list, the agent detail page, and Agent Observability. To change a running agent — including offset behavior on redeploy — see Manage Agents.